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Establishment of human models of the blood-brain barrier (BBB) can benefit research into brain conditions associated with BBB failure. We describe here an improved technique for preparation of a contact BBB model, which permits coculturing of human astrocytes and brain endothelial cells on the opposite sides of a porous membrane.
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Niego, B., Medcalf, R. L. Improved Method for the Preparation of a Human Cell-based, Contact Model of the Blood-Brain Barrier. J. Vis. Exp. (81), e50934, doi:10.3791/50934 (2013).
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The blood-brain barrier (BBB) comprises impermeable but adaptable brain capillaries which tightly control the brain environment. Failure of the BBB has been implied in the etiology of many brain pathologies, creating a need for development of human in vitro BBB models to assist in clinically-relevant research. Among the numerous BBB models thus far described, a static (without flow), contact BBB model, where astrocytes and brain endothelial cells (BECs) are cocultured on the opposite sides of a porous membrane, emerged as a simplified yet authentic system to simulate the BBB with high throughput screening capacity. Nevertheless the generation of such model presents few technical challenges. Here, we describe a protocol for preparation of a contact human BBB model utilizing a novel combination of primary human BECs and immortalized human astrocytes. Specifically, we detail an innovative method for cell-seeding on inverted inserts as well as specify insert staining techniques and exemplify how we use our model for BBB-related research.
The BBB is a specialized interface between the peripheral blood circulation and the central nervous system, crucially responsible for the maintenance of brain hemostasis. It comprises distinct brain microvascular endothelial cells (BECs) which are functionally influenced by few cellular and acellular components (below) to form a tight and dynamic gateway into the brain. Under physiological conditions the BBB restricts the passage of blood cells, plasma components and harmful substances, all potentially neurotoxic, into the brain. In parallel, the BBB selectively exchanges key ions and nutrients (glucose and amino-acids) and metabolic waste products between the brain and the circulation to precisely maintain the brain environment1,2. In recent years it is becoming evident that failure of the BBB occurs in a variety of chronic brain pathologies, such as neurodegenerative or inflammatory-related diseases (e.g. Alzheimer's disease and multiple sclerosis, respectively)3, as well as in acute conditions like ischemic stroke4.
The unique BBB properties of brain endothelial cells (BECs) are largely induced by their cerebral environment5, and in particular by astrocytes6,7. There is a growing understanding that other cell-types, such as pericytes8, neurons and microglia1,3, as well as the basement membrane9, support BECs and form together a functional unit termed the "neurovascular unit" (NVU) which simultaneously couples neuronal metabolic demands to their supplying capillaries10.
The involvement of the BBB in pathological situations underlies numerous attempts to develop in vitro BBB models to assist in BBB-related research11,12. These models aim to mimic as close as possible in vivo BBB characteristics according to the NVU principle. In vitro BBB models generally rely on a monolayer of tight-junction-forming BECs (mainly from bovine13, human14, rat15, mouse16, and porcine17,18 origins), cultured on a porous membrane together with supporting astrocytes (extensively reviewed by Deli et al. 200511).
Astrocytes can be grown in non-contact conditions on the bottom of a tissue culture well, separated from BECs (cultivated on the upper surface of the membrane) by the culture medium yet communicating with BECs via soluble factors16. In more advanced models which better resemble the anatomical structure of the BBB in vivo, astrocytes are maintained in contact conditions and cultured directly on the opposite side of the membrane in close proximity to BECs13,15,17 (Figure 1). This configuration enables physical contact between BECs and astrocytes, established when astrocytes project their processes through the porous membrane. Importantly, for a true contact to occur the pores should be ≥1µm in diameter, since astrocytic end-feet cannot pass through smaller pore sizes (i.e. 0.4 µm)14,15. Notably, contact BBB systems are demonstrated in some studies to be superior to their non-contact counterparts regarding their trans-endothelial electrical resistance (TEER) and endothelial permeability values of various tracers13,17,18. An additional dimension of media flow was recently added in a number of in vitro BBB models to apply shear forces to the endothelium for closer simulation of the brain vasculature12,19.
One technical obstacle to overcome when generating a contact BBB model is the seeding of astrocytes against gravity on the abluminal surface of the porous membrane. Previous protocols13,20, where astrocytes were simply seeded in a drop of media on top of an inverted insert, allowed only short seeding times (i.e. 10 min13 or 2 hr20) which were found in our hands to be insufficient for proper cell attachment. Using this basic method, a longer astrocyte attachment period requires constant monitoring of the inserts by frequent opening of the incubator (causing fluctuations in temperature, pH and humidity) and is also prone to uneven cell seeding due to leakage of media through the pores, especially if pores larger than 1 µm are employed.
Here, we describe a general protocol for preparation of a contact BBB model. Our procedure includes an alternative method for cell-seeding on inverted inserts, which addresses the above mentioned limitations. The method permits undisturbed adherence of astrocytes onto the abluminal membrane surface, in an equilibrated incubator, for an extended period of time. As a result, a uniform seeding of astrocytes is achieved which increases barrier quality and minimizes basal permeability variations between inserts.
As the use of human cells is important for human-relevant research21, we additionally demonstrate in this article the specific utilization of a novel combination of primary human BECs and immortalized human astrocytes for establishment of a contact human BBB model with a high throughput screening capacity. Since viewing of cells on porous membranes can be difficult, we also detail staining techniques which can assist in determination of confluence and cell morphology on the porous membranes. Finally, we exemplify how our human BBB model can be utilized to examine the effect of tissue-type plasminogen activator (t-PA) - a clot busting enzyme which serves as a sole treatment option for acute ischemic stroke - on the BBB.
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1. Cell Culture (3-7 Days Prior to BBB Assembly)
1.1. Primary Human Brain Microvascular Endothelial Cells (BECs)
BECs were commercially obtained. The cells were produced by dispase dissociation of normal human brain cortex tissue and provided frozen at passage 3 (<12 population doublings).
- Substratum: Coat tissue-culture vessels with "Attachment Factor" as per the manufacturer instructions.
- Cell maintenance: Maintain BECs in serum-containing complete medium. For experimentation, culture BECs in complete serum-free medium. Maintain the cells in a humidified 5% CO2, 21% O2 incubator at 37 °C
Note: As an alternative to specialized reagents, 0.1% (w/v) gelatine solution can serve as a coating reagent while DMEM/F-12 supplemented with 15 mM HEPES, 10% (v/v) fetal calf serum (FCS), 2 mM L-glutamine, 50 µg/ml gentamycin, 20 µg/ml heparin and 20 µg/ml endothelial cells growth supplement can be used for BEC maintenance.
- Sub-culturing for experimentation: Split confluent BECs at a ratio of 1:3 to 1:6, depending on the intended time for next use (3-7 days). Change the maintenance medium every 3 days. Use BECs up to 15 passages.
1.2. SVG Human Fetal Astroglial Cell-line
SVG human fetal astroglial cells22 were originally derived from primary cultures of human fetal brain transformed with replication-deficient simian virus 40.
- Maintenance: Culture SVG cells in minimum essential medium with Earle's balanced salt solution supplemented with 20% (v/v) FCS (same serum percentage used to maintain their parental primary cells 22), 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin. Maintain the cells in a humidified 5% CO2, 21% O2 incubator at 37 °C.
- Sub-culturing for experimentation: Split confluent SVGs at a ratio of 1:5 to 1:10, depending on the intended time for next use (3-7 days). Removal of trypsin is not required due to the high content of serum in the medium. Change the medium every 3 days. Use SVGs up to 20 passages.
2. Assembly and Coculturing of the Human In vitro BBB Model (Day 0)
Comment: The human in vitro contact BBB model is prepared according to published protocols 13,23, with modifications.
2.1. Coating of Inserts
- Place tissue-culture inserts (6.5 mm in diameter with polyester porous membrane, 3 µm pore-size) in the wells of the supplied 24-well plate. Coat the luminal surface of the inserts
Note: Basement membrane of brain capillaries contains no collagen I but mainly laminins, collagen type IV isoforms, nidogens, and heparan sulfate proteoglycans9. For this reason, some researchers utilize coating agents such as collagen IV, fibronectin13, or Matrigel (BD biosciences)16 for a more authentic BBB modeling.
2.2. Seeding SVGs on the Underside (Abluminal) Membrane Surface
- Invert the insert and gently fit around the rim of the porous membrane a short piece of elastic silicone tubing ("external tubing"; ~10 mm long, 8 mm internal diameter, 1.6 mm wall), creating essentially a new well above the abluminal surface (Figure 2A).
- The underside of the insert needs to be sealed to avoid media leakage. First, prepare a plug (Figure 2A and 2B) made of a silicone tubing (termed "internal tubing"; ~8 mm long, 3.2 mm internal diameter, 1.6 mm wall; Figure 2B), sealed at one end with a plastic sealing cone (~3mm long; prepared by cutting the bottom of a 0.2 ml polymerase chain reaction (PCR) tube; Figure 2B). This cone not only seals the internal tubing lumen, but also widens its edge for tighter fitting into the insert. Next, using a sterile forceps, insert the silicone plug into the luminal cavity and advance it until it reaches up-to ~1-2 mm from the membrane (Figure 2A).
- Next, seed 4 x 104 SVG cells in 200 µl SVG maintenance medium (section 1.2.2 above) directly into the silicone well above the abluminal membrane surface (Figure 2A). Allow SVGs to adhere for at least 4 hr in the incubator.
Note: To maintain sterility transport the assembled inserts in between two 6-well plates (one plate inverted over the other; Figure 2C) to minimize exposure to unfiltered air during the procedure. Silicone tubes and plugs are washed in ethanol and autoclaved for later use.
Note: Use plugs (section 2.2.2) only for inserts with membrane pore sizes larger than 1 µm. Membranes with pore diameter ≤1 µm rarely leak and require only the fitting of the external silicone tubing (section 2.2.1). In addition, some coating agents other than collagen I (see note to section 2.1) may prevent leakiness even through 3 µm pores. Determine this empirically.
- In order to monitor the adherence state of your astrocytes, seed in parallel into a standard 96-well an identical volume of the astrocyte cell-suspension. This well has the same surface area as the 6.5 mm insert (0.33 cm2) and permits easier visualization of cells by phase-contrast microscopy than the insert membrane. When you observe sufficient adherence of the astrocyte in the control 96-well, the astrocytes have adequately adhered also to the insert membrane.
- When astrocytes have sufficiently adhered in the control 96-well, transfer the insert assemblies to the tissue culture hood (see note above) and gently remove the external silicone tubing and plugs. Return inserts to the normal (i.e. upright) orientation into the supplied wells containing 800 µl/well of BEC maintenance medium (section 1.1.2 above).
2.3. Seeding of BECs into the Luminal Chamber above the Astrocytes
Seed 2x104 BECs in 200 μl onto the collagen-coated luminal surface and return the inserts into the incubator.
2.4. Coculturing
Coculture the SVG/BEC-seeded inserts for 3 days in BEC medium without media change before experimentation.
3. Experimentation with the Human BBB Model (Day 3)
- To stimulate the in vitro BBB first wash cells by replacing the abluminal and luminal media with 600 µl and 100 µl of serum-free BECs medium, respectively.
- Aspirate the luminal medium again and replace with 100 µl of serum-free medium with stimulating agents.
Note: If stimulation of the abluminal chamber is required (to activate the in vitro BBB from the astrocytic compartment), aspirate the abluminal medium and replace with 600 ul of serum free medium with stimulating agents.
- Test each experimental group in triplicate (hence, if luminal stimulation is performed, which requires 3 x 100 µl, we recommend diluting the reagents to their final concentrations in 350 µl of serum-free medium per group to minimize pipetting errors between wells).
- Perform standard paracellular and/or transcellular permeability assays of labeled tracers 16,24 (for the latter we use fluorescein isothiocyanate (FITC)-conjugated albumin) and/or measurement of TEER 13,16,25 as previously described.
To account for fluctuations in baseline permeability between experiments, analyze changes in permeability relative to a coated insert without cells (which serves as a reference for maximal permeability) and to the vehicle-treated group using the formula: permeability (% of max) = (permeability value of experimental insert - average permeability value of the vehicle group) / (permeability value of the blank insert - average permeability value of the vehicle group) X 100.
4. Visualization of Cells on Porous Membranes
Comment: Unstained cells on insert membranes are difficult to observe by phase contrast or differential interference contrast (DIC) microscopy since the membrane is quite opaque and interferes with light transmission. To deal with this issue we developed various staining procedures of cells on inserts, which greatly enhance cell appearance on the membrane. Generally, stain the insert membranes while still attached to the insert, in the wells. The membranes should be removed only at the end of the staining procedure for mounting purposes.
4.1. Hematoxylin Staining
- Fix inserts with ice-cold 4% (w/v) paraformaldehyde (PFA) in phosphate-buffered saline (PBS; 130 mM NaCl, 10 mM Na2HPO4, 10 mM NaH2PO4, pH 7.2) for 20 min. Wash once in PBS.
- Submerge the insert in 0.1% Mayer's hematoxylin solution for 2 min.
- Gently wash the insert with tap water (by submerging the insert in a beaker containing tap water and gently pipetting the excess water out). Transfer the insert to Scott's tap water solution (2 g/L NaHCO3, 20 g/L MgSO4) for ~20 sec until the desired blue color develops. Wash the insert again with tap water (section 4.1.3). An optional eosin stain can be introduced here by dipping the insert in 1% eosin solution for 10 sec followed by a tap water wash.
- Air-dry the insert membrane. Using a scalpel (cutting around the membrane edge) remove the membrane and mount it on a glass slide for bright-field imaging
Note: If di-N-butylphthalateinxylene (DPX) mounting medium is utilized wash the insert with 100% ethanol followed by xylene before removing and mounting the membrane.
4.2. Scanning Electron Microscope (SEM)
- Fix inserts with ice-cold 4% (w/v) PFA in PBS for 20min. Wash three times in PBS.
- Post-fix the membranes for 30 min in 1% (w/v) osmium tetroxide in MQH2O.
- Wash inserts in water (section 4.1.3) and dehydrate in an escalating ethanol gradient (50%, 70%, 90% for 5 min each, then 3x with 100% ethanol for 10 min per interval).
- Treat dehydrated inserts with a mixture of hexamethyldisilazane (HMDS):ethanol (1:2 then 2:1 for 5 min).
- Treat inserts with 100% HMDS for 5 min, air-dry and store under desiccated conditions.
- Remove the membrane using a scalpel (cutting around the membrane edge). Examine membranes by scanning electron microscopy.
4.3. Immunofluorescence
- Fix inserts with ice-cold 4% (w/v) PFA in PBS for 20 min, wash once in Tris-buffered saline (TBS; 50 mM Tris-HCl pH 7.6, 154 mM NaCl, 0.22 µm filtered ).
- Perform immunostaining of the cells on the insert membrane as previously described26.
- Remove the membrane using a scalpel (cutting around the membrane edge) and mount it on a glass slide with fluorescence mounting medium. Examine membrane by fluorescent microscopy.
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Representative Results
In order to establish a human, contact BBB model we had to cultivate SVGs and BECs on porous membranes with a 3 µm pore-size, shown to permit passage of astrocyte end-feet for contact with endothelial cells14,15,27,28. A schematic representation of the complete contact model is illustrated in Figure 1 (left illustration). The main technical challenge presented by a contact system is the need to seed astrocytes against gravity on the abluminal surface of the membrane. We have succeeded in this task by principally creating a removable silicone well on top of the abluminal membrane surface while preventing leakage of media with an additional silicone plug inserted into the luminal cavity (Figure 2). The method allowed an optimal, uninterrupted seeding period of SVGs (at least 4 hr), which in turn became strongly attached to the porous surface with minimal cell loss. Indeed, 3 days post-seeding both SVGs and BECs appeared confluent and covered the membrane uniformly, as judged from images of hematoxylin-stained inserts (see section 4.1; Figure 3A, left panels). Additionally, both cell types maintained their cell-specific markers on porous membranes (a diffuse staining pattern of glial fibrillary acidic protein in SVGs22 and expression of von Willebrand factor in Wiebel-Palade bodies and cytoplasmic vesicles in BECs; Figure 3A, right panels), observed by immunofluorescence (section 4.3). Furthermore, from SEM imaging (section 4.2) it became apparent that SVGs and BECs were capable of growing directly over the membrane pores (Figure 3A, middle panels, arrow heads), a phenomenon which artificially contributes to the physical barrier function of in vitro BBB contact models13,28. Functionally, in the presence of SVGs the endothelial permeability value (Pe)16,24 of fluorescent albumin28 improved ~4-fold relative to BECs alone (P<0.01; n=4), from 0.574±0.199 x10-3 to 0.126±0.028 x10-3 cm/min without or with astrocytes (SVGs), respectively. These albumin Pe results are comparable to another study using mouse BECs with rat C6 glioma cells29. Our seeding protocol was successfully applied also to freshly-isolated primary mouse BECs and primary mouse astrocytes grown on 1 µm pores. In this mouse contact model we obtained a reasonable physical barrier for the small hydrophilic tracer sodium fluorescein (Pe=0.83±0.22 x10-3 cm/min; n=3). Hence, our seeding method can be extended for preparation of BBB models from other species.
The most fundamental feature of a contact BBB model is the ability of astrocytes to project their end-feet through the membrane pores to physically contact endothelial cells. A few studies made use of scanning electron microscopy to identify astrocytes processes which pass from the abluminal membrane surface (where astrocytes are seeded) to the luminal surface, as a proof of principle that contact between astrocytes and EC is plausible14,15,27. Following these studies we imaged by SEM the luminal membrane after seeding SVGs on the abluminal surface. As shown in Figure 3B, astrocytic (SVG) end-feet could be observed passing through 3 µm pores into the luminal compartment, confirming that contact can potentially exist between SVGs and BECs seeded on pores of this dimension. Collectively, our seeding method was simple, efficient, and yielded an authentic contact BBB model.
We have utilized our human contact BBB model to explore the effect of t-PA, a fibrinolytic agent, on the BBB. A number of articles have suggested that t-PA may be able to induce opening of the BBB during its intravenous administration for the treatment of acute ischemic stroke30-33. This interaction between t-PA and the BBB may contribute to bleeding complications associated with t-PA-induced thrombolysis4,34,35 and is thus a subject for current research efforts. Our studies in vitro confirmed that t-PA indeed increased the permeability of the intact BBB in a concentration- (Figure 4A) and time-dependent manner (Figure 4B), which were both within a pharmacologically-relevant range36,37. Furthermore, dramatic morphological changes of SVG astrocytes could be observed on the porous membranes post exposure to t-PA (Figure 4C), suggesting that astrocytic responses to t-PA underlie t-PA-induced BBB opening. Subsequent investigation identified that t-PA stimulates astrocytes via activation of plasminogen - its natural substrate - into the potent and broad-spectrum protease plasmin. Furthermore, t-PA and plasmin were found to activate Rho-kinase (ROCK) signaling in astrocytes, and blockade of the ROCK pathway prevented t-PA-induced BBB opening28. This is a good example of how utilization of BBB models can lead to identification of novel biological processes and therapeutic opportunities.
Figure 1. Insert-based configurations of in vitro BBB models. Schematic representations of static (without flow) in vitro BBB models. In basic models brain endothelial cells (ECs; orange) are cultured alone on a porous membrane (yellow). In more advanced approaches ECs are cocultured with astrocytes (green). Astrocytes can be grown either in non-contact conditions, on the bottom of the well (right), or on the abluminal surface of the porous membrane in contact conditions (left). Click here to view larger image.
Figure 2. An improved method for astrocyte seeding on the abluminal membrane surface for preparation of a contact BBB model. (A) The insert is inverted, a short piece of silicone tubing ("external tubing") is assembled around its perimeter and a silicone plug fitted into the luminal cavity. This assembly creates a well on top of the abluminal surface and prevents media from leaking through the pores. Cells can be seeded in the well and allowed to adhere undisturbed for extended periods of time (right) (B) The silicone plug is prepared from another piece of silicone tubing ("internal tubing"), sealed at one end with a sealing cone which is inserted into its cavity. (C) Once seeded, the assembled inserts are transported in between two 6-well plates (one plate inverted over the other) to minimize the risk of contamination. Click here to view larger image.
Figure 3. Visualisation of brain endothelial cells (BECs) and astrocytes on a 3 µm porous membrane. A) Hematoxylin (left panels), scanning electron microscope (SEM; middle panels) and immunofluorescence images (right panels) of SVGs (on the abluminal surface, 40,000 per 6.5 mm insert, top panels) and BECs (20,000 per 6.5 mm insert on the collagen I-coated, luminal surface, bottom panels) grown on a 3 µm porous membrane for 3 days (without coculturing). Both cell types reach confluence within this time frame and maintain their cell-specific markers (glial fibrillary acidic protein (GFAP) for SVG and von Willebrand factor (vWF) in Wiebel-Palade bodies for BECs, right panels) on the membrane. Arrow heads in the SEM panels further show the ability of both cell types to directly grow over and cover the pores, while arrows represent cell nuclei. Scale bars on SEM images represent 25 µm (top) or 20 µm (bottom). Scale bars on immunofluorescence images represent 40 µm. (B) SEM images of SVG end-feet passing through 3 µm pores into the luminal (BEC) surface 3d post seeding of SVGs (alone) on the abluminal membrane surface. These images validate the potential for development of true contact between SVGs and BECs in our human BBB model. Scale bars represent 6 µm on the left panel and 12 µm on the right panel. Click here to view larger image.
* For further clarification of the cells position please refer to Figure 1, left panel.
Figure 4. tissue-type plasminogen activator (t-PA)-mediated increase in BBB permeability is driven by morphological changes of astrocytes. (A) Increasing t-PA concentrations were added to the luminal chamber of the human in vitro BBB and permeability was assessed 24 hr later by passage of fluorescent albumin into the abluminal chamber. t-PA increased BBB permeability in a concentration-dependent manner. n=4-6 for all groups but t-PA 10nM, where n=2. ***P<0.001 compared to all other groups by one way ANOVA with Newman-Keuls post hoc. (B) t-PA (1 µM) was added to the luminal chamber of the human in vitro BBB for 8 hr and 24 hr before permeability assessment. As shown, t-PA-mediated BBB opening was already substantial 8 hr post stimulation (70.26% of the maximum at 24 hr) but a further increase was observed at 24 hr. n=6, ***P<0.001, **P<0.01compared to zero by one way ANOVA with Newman-Keuls post hoc. In both (A) and (B), bars/data points represent mean±SEM. (C) Representative bright-field images of Haematoxylin-stained SVGs (top panels) or scanning electron micrographs of SVGs (bottom panels) on polyester insert membranes (3 µm pore size) 24 hr post treatment with vehicle or t-PA (1 µM). Pronounced t-PA-induced morphological changes and disruption of the SVG monolayer integrity can be observed, suggesting that astrocytic responses to t-PA underlie the effect of t-PA on BBB permeability. Images are representative of two independent experiments. Scale bars on SEM images represent 61 µm (left) or 120 µm (right). Click here to view larger image.
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Medical research into brain pathologies suffers much translational difficulty. In the area of acute ischemic stroke, for example, many drugs which showed great promise in animal models failed at the clinic38,39. The reasons for these disappointing results are diverse and include infidelity of the preclinical test systems to the human stroke scenario and overstatement of results obtained from animal studies21. One strategy to improve the predictive value of preclinical findings to the clinical phase is additional development of human cell-based in vitro models; these may provide potent and cost-effective tools for screening novel drugs and mechanisms and allow better focusing on clinically-relevant avenues 21. Within this context, we described here in detail the process undertaken in our laboratory for the establishment of a human contact BBB model utilizing a novel combination of human immortalized astrocytes (SVGs) and human primary BECs for examination of stroke-related phenomena.
Under static conditions (without flow), contact BBB models (Figure 1) resemble best the in vivo anatomical structure and are shown to be superior in their barrier properties13,17,18. Importantly, true contact between BECs and astrocytes occurs only on membranes with larger pore diameter (≥1 µm), which permit passage of astrocytic end-feet and subsequent induction of tighter (less permeable) BEC monolayers14,15,27. However, the seeding of astrocytes on inverted inserts with higher pore size - the core technical element in the preparation of a contact BBB model - presents a double technical challenge, since in addition to the limited medium volume which can be added to the inverted 6.5 mm inserts (~50 ul), the membranes are also prone to leakage of medium due to their large pore diameter. These limitations can make the astrocytic seeding awkward and inconsistent.
Our innovative technique for seeding astrocytes on inverted inserts with 3 µm pores offers a simple yet effective solution for this procedure. By creating a removable, impermeable silicone well on top of the abluminal surface we greatly improved the astrocytes' ability to successfully adhere to the porous membrane. This enables uniform growth of astrocytes on the abluminal surface and allows reproducible preparation of a large number of inserts for simultaneous screening of many experimental parameters (for example, our recently performed screening of the effect of five different plasminogen-activators on the BBB28) . Hence, despite its simplicity, we found our method to dramatically improve the throughput and quality of our research using this contact BBB model. One limitation of our astrocyte seeding method is the requirement for extra manual handling of the inserts, which harbor thin (10 µm thick) membranes. Excessive or rough handling can cause micro-tears and damage to the membrane. Hence, the use of soft and flexible silicone tubing is essential as the assembly/disassembly has to be performed with minimal force.
Regarding its overall concept, our human BBB model was established based on a previously-described protocol14,23 which originally utilized primary human cells. Despite their superiority for BBB modeling, one drawback in employment of primary human brain cells is the inability to routinely access live human brain tissue for regular production of primary BECs and astrocytes and/or the cost involved in commercially obtaining them. Since many BBB models successfully substitute primary astrocytes with immortalized astrocytes (for example with a rat C6 glioma cells29), it was reasonable to employ SVG astrocytes22 as a novel, abundant and cost-effective option for human BBB modeling. The ability of SVGs to positively influence the BEC barrier function validated in principle their suitability for BBB studies. In contrast, we chose to keep primary BECs in our BBB model for their critical role in the BBB in vivo1,2,9. Importantly, prolonged culturing of primary BECs can result in de-differentiation and deterioration of their BBB-specific phenotype. This indeed may be evident in our system by a rather high endothelial permeability values for albumin. For this reason, we have used our model mainly in a relative fashion28 (Figure 4), taking into account its elevated basal permeability. If a very tight human BEC monolayer is desired the barrier function can be further improved by addition of chemicals such as the glucocorticoid hydrocortisone17 or cyclic AMP with phosphodiesterase inhibitors13. Immortalized human BECs, such as the hCMEC/D3 cell line40, could also be employed for their ability to better maintain tight junctions over time. Yet, BEC cell-lines often display reduced barrier qualities compared to low-passage primary BECs41,42 and needs to be carefully considered because of other potential changes (i.e. altered expression of cell-surface receptors).
Collectively, our modification of the astrocytic cell type (i.e. the employment of SVGs) and the substantial improvement to the astrocyte seeding protocol resulted in generation of a reproducible, easy to assemble and authentic human contact BBB model. Bearing in mind its limitations, this system can serve as a useful tool for in vitro investigation not only of acute ischemic stroke, as we exemplified here28, but also of several other brain pathologies involving modulation of the BBB.
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Authors have nothing to disclose.
This study was funded by grants awarded to R.L.M. from the National Health and Medical Research Council of Australia (grant # 606658).
|Catalog Number
|Attachment Factor
|Cell-Systems Corporation
|Brain microvascular endothelial cells (BECs), primary, human
|Cell-Systems Corporation
|Complete medium
|Cell-Systems Corporation
|Supplemented with CSC JetFuel
|Complete serum-free medium
|Cell-Systems Corporation
|Supplemented with CSC RocketFue
|DMEM/F-12 with 15 mM HEPES
|Life Technologies
|Endothelial cells growth supplement (from bovine origin)
|External silicone tubing
|Pumpsil brand, 8 mm internal diameter, 1.6 mm wall
|Foetal calf serum
|Gentamycin sulfate
|Life Technologies
|Heparin sodium
|1,000 U/ml
|Hexamethyldisilazane (HMDS)
|Human brain microvascular endothelial cells
|Cell-Systems Corporation
|Internal silicone tubing
|Pumpsil brand, 3.2 mm internal diameter, 1.6 mm wall
|Life Technologies
|Mayer's hematoxylin solution
|Amber Scientific
|Minimum essential medium with Earle's balanced salt solution
|HyClone Laboratories
|Life Technologies
|Rat collagen I
|Cultrex brand
|Tissue culture inserts
|Corning Life Sciences
|Transwell brand, 6.5 mm in diameter, with polyester porous membrane, 3 µm pore-size
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Online and Compositional Learning of Controllers with Application to Floor Heating
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Controller synthesis for stochastic hybrid switched systems, like e.g. a floor heating system in a house, is a complex computational task that cannot be solved by an exhaustive search though all the control options. The state-space to be explored is in general uncountable due to the presence of continuous variables (e.g. temperature readings in the different rooms) and even after digitization, the state-space remains huge and cannot be fully explored. We suggest a general and scalable methodology for controller synthesis for such systems. Instead of off-line synthesis of a controller for all possible input temperatures and an arbitrary time horizon, we propose an on-line synthesis methodology, where we periodically compute the controller only for the near future based on the current sensor readings. This computation is itself done by employing machine learning in order to avoid enumeration of the whole state-space. For additional scalability we propose and apply a compositional synthesis approach. Finally, we demonstrate the applicability of the methodology to a concrete floor heating system of a real family house.
KeywordsModel Predictive Control Linear Temporal Logic Controller Synthesis Home Automation Heat Exchange Coefficient
1 Introduction
Home automation includes the centralized control of a number of functionalities in a house such as lighting, HVAC (heating, ventilation and air conditioning), appliances, security locks of gates and doors as well as other systems. The overall goal is to achieve improved convenience, comfort, energy efficiency as well as security. The popularity of home automation has increased significantly in recent years through affordable smartphone and tablet connectivity. Also the emergence of "Internet of Things" has tied in closely with the popularization of home automation. In particular, several devices may be connected through a home network to allow control by a personal computer, and may allow remote access from the internet.
The connectivity in the home enables new, intelligent and personalized control strategies or (and across) activities in the house. One novel approach which is being developed and applied in the on-going EU FP7 project CASSTING1 is that of game theory. Empowered with efficient techniques and tools, game theory comes with the promise of automatic synthesis of improved, optimal and personalized control strategies produced on demand by the user herself. In fact, the tool Uppaal Tiga has already been successfully2 applied to user-directed and user-demanded synthesis of control strategies for lighting in a house, and been implemented in a complete tool-chain on a Raspberry Pi [12].
Within the CASSTING project, we collaborate with the Danish company Seluxit3 offering complete home automation solutions. The focus is on the floor-heating system of a family house, where each room of the house has its own hot-water pipe circuit. These are controlled through a number of valves based on information about room temperatures communicated wirelessly (periodically due to energy considerations) from a number of temperature sensors. In the present system, a simple "Bang-Bang"-like strategy is applied. There are though several problems with this strategy, as experienced by the house owner: it completely disregards the interaction between rooms in terms of heat-exchange, the impact of the outside temperature and weather forecast as well as information about movements in the house. Taking this knowledge into account should potentially enable the synthesis of significantly improved control strategies.
For the control synthesis of the lighting system, timed games and Uppaal Tiga proved sufficient. However, in order to control a floor-heating system, we must take into account continuous (temperature) as well as stochastic aspects (outside temperature, movements). Hence we need to be able to (efficiently) synthesize strategies for stochastic hybrid games.
A promising starting point is the recent branch Uppaal-Stratego [5, 6], which allows for the synthesis of safe and near-optimal strategies for stochastic timed games using a combination of symbolic synthesis and reinforcement learning. The tool has recently been extended to stochastic hybrid games with a successful application to the synthesis of strategies for battery aware scheduling problems [14] as well as safe and optimal adaptive cruise controllers for cars [9].
Facing the floor heating case study of CASSTING, direct application of Uppaal-Stratego does not scale: due to the enormous number of control modes it is virtually impossible to learn optimal control. Instead, we propose a novel on-line synthesis methodology, where we periodically—and on-line—learn the optimal controller for the near future based on the current sensor readings. For additional scalability, we propose and apply a novel compositional synthesis approach. As we shall see this combination allows us to significantly improve upon the currently applied "Bang-Bang" control strategy.
Related Work. In [10, 11] a method and tool (PESSOA) is presented for synthesizing controllers for cyber-physical systems, represented by a set of smooth differential equations and automata given a specification in a fragment of Linear Temporal Logic (LTL). In [8] a class of hybrid systems that involve random phenomena, in addition to discrete and continuous behaviour are considered, and abstraction techniques are presented and applied to the synthesis of controllers. In [13] the authors provide an abstraction-refinement method for synthesis of controllers for discrete, stochastic dynamical systems with respect to LTL objectives. In [7] a number of benchmarks for hybrid system verification has been proposed, including a room heating benchmark. In [3] Uppaal SMC was applied to the performance evaluation of several strategies proposed in the benchmark. In [4] a combination of Uppaal SMC with ANOVA has been made for efficient identification of optimal parameters of the various control strategies.
Our online approach may be seen as an instance of model predictive control or receding time horizon control for hybrid systems (see e.g. [2]) where the optimal solutions are already very expensive to compute. We tackle even a more general class of systems (including stochasticity in particular) and apply a learning heuristic that is cheaper on the cost but does not guarantee optimality.
The main novelty of our work, compared to the previous research, is that we address an industrial-size case with its full complexity, where the already studied methods and approaches do not scale. It is the combination of online learning approach, employment of the very recent tool support and the compositional approach that allowed us to significantly improve upon the performance of the current controller used for the floor heating system in the existing house.
2 Switched Control Synthesis
We use a one-room heating control problem as a running example to demonstrate our techniques in a simple setting: we model the problem, explain the necessary theory behind the model, show how the model fits the theory and show how Uppaal Stratego can be used to solve the problem.
Figure 1b shows such differential equation with heater step functions modelled in Uppaal Stratego as hybrid automaton with two discrete modes. The continuous dynamics of T(t) is typeset as an invariant constraint over the clock variable T derivative under the respective modes. The periodic behaviour of the controller is enforced by the invariant Open image in new window and guard Open image in new window over clock x with default derivative of 1. For the sake of simplicity, we assume static outdoor temperature fixed to a specific value and modelled by the constant floating point variable Te. All model variables (their types and initial values) are declared as C structures in Fig. 1a. The window step function A(t) is modelled in Fig. 1c as stochastic automaton with transitions between "Open" and "Closed" modes and changing the floating point variable A. Thus the window process can change the value of A discretely between values Aclosed and Aopen at any moment with uniform probability distribution over time, but only at intervals specified by a user profile. The profile is stored in arrays closedL/U and openL/U denoting the lower and upper bounds of time intervals when the switch may happen. For example, one can read the profile arrays by columns: the window starts and stays closed during the night time, but it will open somewhere between 6 and 7 o'clock in the morning and close between 7 and 8 o'clock, then it will open again between 11 and 12, and close between 12 and 13, etc.
Definition 1
- 1.
\(\mathcal {C}\) is a controller with a finite set of (controllable) modes C,
- 2.
\(\mathcal{U}\) is the environment with a finite set of (uncontrollable) modes U,
- 3.
\(X=\{x_1,\ldots ,x_n\}\) is a finite set of continuous (real-valued) variables,
- 4.
for each \(c\in C\) and \(u\in U\), \(\mathcal{F}_{c,u}:\mathbb {R}_{>0}\times \mathbb {R}^X\rightarrow \mathbb {R}^X\) is the flow-function that describes the evolution of the continuous variables over time in the combined mode (c, u), and
- 5.
\(\delta \) is a family of density functions, \(\delta _{\gamma }:\mathbb {R}_{\ge 0}\times U\rightarrow \mathbb {R}_{\ge 0}\), where \(\gamma =(c,u,v)\in C\times U\times \mathbb {R}^X\). More precisely, \(\delta _{\gamma }(\tau ,u')\) is the density that \(\mathcal{U}\) in the global configuration \(\gamma =(c,u,v)\) will change to the uncontrollable mode \(u'\) after a delay of \(\tau \) 4.
We shall assume that among the continuous variables X, there is a variable \(\mathsf{time}\) measuring global time, i.e. \(\mathcal{F}_{c,u}(\tau ,v)(\mathsf{time}) = v(\mathsf{time})+\tau \) for any mode-configuration (c, u). In the above definition, the syntactic specification of flow functions—e.g. using ODEs—has been left open. In the game \(\mathcal{G}\), the controller \(\mathcal {C}\) will only be permitted to change controllable mode at time-points being a multiple of some given period P (hence the term switched control). In contrast, the environment \(\mathcal{U}\) will change its uncontrollable mode according to the family of density functions \(\delta _{\gamma }\).
Example 1
In our one-room example, the controllable modes are Open image in new window and Open image in new window with controllable transitions (using solid lines) between them, the uncontrollable are Open image in new window and Open image in new window with uncontrollable transitions (using dashed lines). We also have a number of continuous variables: temperature T and clocks t, x and w. The differential equations together with discretely changing variables are part of the flow-function definition. \(\lhd \)
Now let \(\mathbb {C}\) denote the set of global configurations \(C\times U\times \mathbb {R}^X\) of the game \(\mathcal{G}\). Then a (memoryless) strategy \(\sigma \) for the controller \(\mathcal {C}\) is a function \(\sigma :\mathbb {C}\rightarrow C\), i.e. given the current configuration \(\gamma =(c,u,v)\), the expression \(\sigma (\gamma )\) is the controllable mode to be used in the next period.
In fact, under a given strategy \(\sigma \) the game \(\mathcal{G}\) becomes a completely stochastic process \(\mathcal{G}\upharpoonright \sigma \), inducing a probability measure on sets of runs. Thus, if \(H\in \mathbb {N}\) is a given time-horizon, and D is a random variable on runs—e.g. measuring the integrated deviation of the continuous variables wrt. given target values—then \(\mathbb {E}^{\mathcal{G},\gamma }_{\sigma ,H}(D)\in \mathbb {R}_{\ge 0}\) is the expected value of D with respect to random runs of \(\mathcal{G}\upharpoonright \sigma \) of length H starting in the configuration \(\gamma \). We want to obtain a strategy \(\sigma ^H\) which minimizes (or maximizes) this expected value.
Example 2
For example, the synthesized 24 h strategy using the "naive" learning method yields the distance of 2750 on average as shown in Fig. 2b. The result is even more improved by the "splitting" learning method in Fig. 2c where the temperature oscillates around the goal very closely. \(\lhd \)
Uppaal Stratego offers four learning methods focusing on various parts of the model, therefore we consider the quality and the cost of each method before we focus on our industrial-scale example. Table 1 shows a summary of the evaluation of various methods on two variants of a one-room example: the purely dynamical model is shown in Fig. 1 and another one that has an extra counter incremented at each period P. The result is that among the offline methods (discussed so far) the "splitting" method provides the smallest distance solution, however it is costlier than others in CPU time and memory. The right side Table 1 shows that if we add a period counter to our model, then other methods dominate and the "splitting" method is no longer as good and the "naive" computation costs significantly less. Offline-6 section (strategy for six days) requires twice as many resources as offline-3 (strategy for three days) which means that a linear number of resources is needed in terms of duration of the strategy while using the same number of runs, but the quality (distance) degraded almost four times with a period counter.
2.1 Online Synthesis
Performance evaluation of one room controller synthesis: offline-3(-6) methods synthesize strategy for entire 72 h (144 h respectively) at once, strategy distance is evaluated on 70 simulations; online-3 methods synthesize a strategy for 5 periods of 15 min ahead and repeat synthesis and execution until 72 h are covered, the distance is averaged over 70 online simulations.
Example 3
- 1.
Synthesize a strategy capable of adapting for 5 periods ahead where LastTime starts with 0: Open image in new window
- 2.
Simulate the system for 1 period using the strategy S and record its last state: Open image in new window
- 3.
Create a copy of the original model and replace the initial state with the recorded state from the simulation above.
- 4.
Increment LastTime by P and repeat the synthesis and simulation from step 1 until a required number of periods is simulated.
- 5.
Record the final value of the distance variable D.
The short trajectories from step 2 are then stitched together to produce a continuous trajectory of the entire 3 day simulation. An example result of the first 24 h is displayed in Fig. 2d which is also comparable to other strategies. The online-3 section of Table 1 shows the averages of the recorded distances together with the overall synthesis effort for entire 3 day emulation. The encouraging result is that the short strategy synthesis takes only 4–8 s and the overall quality of any online synthesis method is very close to the best and expensive offline-3 outcome (the offline "splitting" method). \(\lhd \)
3 Floor Heating Case Study
The house consists of 11 rooms, all of them heated with a floor heating system where each room has its own pipe circuit that can be either open (hot water circulates) or closed (water does not circulate). The opening and closing of the circuits is executed by a number of valves located in room \(R_7\). Every 15 min a wireless temperature sensor in each room wakes up and reports its current reading. Currently the bang-bang strategy runs every 15 min: it collects the temperatures of all rooms, if a given room temperature is below its target temperature (setup by the user) it opens the corresponding valve and similarly if the temperature is above target it closes the valve.
- 1.
Heating capacity of the system. The heating system can only provide a limited water pressure to make the water circulate within the pipes. If too many valves are open, the water will only cycle in the shortest pipes. This is especially a problem in the living room \(R_{11}\), as it has the longest pipe circuit, and is also the most important room for the user, meaning that the temperature of this room should be maintained close to the user's wish. A smart heating system should take the heating capacity into a consideration and never exceed it.
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Behaviour of the doors. The heat exchange between rooms are significantly affected by whether doors between the rooms are open or closed. The house is not equipped with door sensors, so the position of each door is unknown. This means that the control strategy has to work under a partial observability and the status of each door can be inferred only indirectly by observing the speed of heat propagation via temperature changes in the rooms.
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Physical layout of the pipe circuits. Finally, as the valves are all located in room \(R_7\), the pipes leading to some of the remote rooms necessarily pass under under rooms. Hence e.g. opening a valve for the room \(R_2\) will contribute also to minor increase of the room temperature in rooms \(R_3\), \(R_5\), \(R_4\) and \(R_6\) under which the pipe circuit is placed.
In our thermodynamic model of the floor heating, we take all these factors in consideration. The aim of the controller program is to optimize the user comfort and satisfaction according to some measure of how far the actual temperatures are from some goal temperature.
Floor Heating Scenario as a Stochastic Hybrid Game. The floor heating scenario with \(n\) rooms and \(m\) doors is a stochastic hybrid game \(\mathcal{G}_{n,m} = (\mathcal {C},\mathcal{U},X,\mathcal{F},\delta )\), where the controller \(\mathcal {C}\) has a finite set of controllable modes \(V=\mathbb {B}^{n}\) given by all possible valve opening/closing combinations. The environment \(\mathcal{U}\) has a finite set of uncontrollable modes \(D=\mathbb {B}^{m}\) given by all possible door opening/closing combinations. We assume that \(\mathcal{U}\) given \(\delta \) can switch among modes with equal probability at every period. The state variables in X are given by the room temperatures \(\{T_1,\dots ,T_{n}\}\) and the outside temperature \(T_{\textsf {env}}\).
3.1 Experiments
Controllers. In the following we introduce a number of controllers which we use in our experiments. We present the current controller operating in the house, two controllers proposed by engineers and the controller synthesized using online synthesis and Uppaal-Stratego.
Bang-Bang Controller. The bang-bang controller is currently running in the physical house and after each reading of room temperatures T, it simply opens the valves of every room i where \(T_i < T^g_i\) and leaves the remaining valves closed.
Capacity Aware Bang-Bang Controller. The main problem with the bang-bang strategy is that if all rooms are below their target temperatures, it simply opens all valves in the house, violating the restriction on the maximal capacity of the floor heating system. The capacity aware bang-bang controller, at each time where a decision is to be taken, orders in descending order all rooms according to their individual distance function, given for a room i by \(W_i\cdot (T^i_g - T^i)^2\) where \(W_i\) is the given priority of room i, and then opens in this order the valves of all rooms that are below their target temperatures (as the normal bang-bang controller) but only until the maximum capacity is exceeded.
Brute-Force Controller. This is an online controller with short horizon 1 that for n valves by brute-force explores all possible \(2^n\) valve combinations and selects a valve combination that minimizes the distance function. The controller operates as follows: after the current reading of the temperatures T and the valves configuration v, it guesses a random door mode d and using this information it computes the expected temperatures \(T'\) exactly after P time units (recall that in our case study we fixed the period to \(P=15\)); next the controller considers all \(2^n\) possible valve configurations and computes the predicted room temperatures \(T''\) at time 2P. The controller then returns the valve configuration that minimizes the distance function dist. Note that already for the short horizon 1, the computation of the brute-force controller takes over 170 seconds, so exploring by brute-force all \(2^{2n}\) combinations (in our case \(n=11\)) needed for the short horizon of 2 is impossible due to the 15 min duration of the period.
Stratego Online Controller. (Stratego-ON) The controller is synthesized by Uppaal Stratego using the online strategy synthesis methodology introduced in Sect. 2 with short horizon of 3. The aim is to learn the optimal valve configurations for several steps ahead using machine learning methods and hence avoid the exhaustive search done by the brute-force controller.
The stability and vacation scenarios are then subject to two different weather profiles, a mild winter where the outside temperature behaves according to real data from the Aalborg airport from 03.02.2015, 00:20 to 07.02.2015, 23:50 where the outside temperature ranges between 2 to 5 \(^{\circ }\)C, and a tough winter using the data from the Aalborg airport from 14.02.2015, 00:20 to 17.02.2015, 23:50 where the outside temperature ranges between -10 and 6 \(^{\circ }\)C. We also consider the spring scenario where the outside temperature is modelled using a sinusoid \(T^{\textsf {env}}(t) = 7*sin(2*pi/60*24*t)+19\) such that most of the time the outside temperature is below the target room temperatures but the peak environment temperature during the middle of the day exceeds the target room temperatures.
In all scenarios, a fixed profile when a specific door is closed or open is used, corresponding to the typical behaviour of the owner of the house. Note that none of the controllers is aware of this fixed door profile.
Evaluation of controllers for 5 and 11 rooms of the house (see Fig. 3). The simulation has a horizon H of 3 days, and a short horizon h of 3 periods. Temperatures are read every 15 min.
4 Compositional Synthesis
Although online learning is an important step towards the scalability of our approach, it does not enable us to learn small horizon strategies of sufficient quality for the full version of the floor heating case study. Even though we have decreased the horizon, the branching factor is enormous: for each period we have to learn the optimal setting of 11 valves, i.e. the optimal of \(2^{11}\) modes. Given a horizon h, this means that we have to learn the optimal sequence of modes out of \(2^{11h}\) possible sequences. Clearly, this becomes infeasible for small h.
However, often the set of modes C will be a product of two (or more) sub-modes, i.e. \(C=C_1\times C_2\); e.g. in the floor heating case study we may split the 11 valves into two subsets i.e. valves 1 to 5 and valves 6 to 11. This suggests the possibility of a compositional approach for the synthesis of \(\sigma ^h\) based on the synthesis of two sub-strategies \(\sigma ^h_1:\mathbb {C}\rightarrow C_1\) and \(\sigma ^h_2:\mathbb {C}\rightarrow C_2\), with \(\sigma ^h(\gamma )=(\sigma ^h_1(\gamma ),\sigma ^h_2(\gamma ))\).
Given an initial sub-strategy \(\sigma ^0_1:\mathbb {C}\rightarrow C_1\), the game \(\mathcal{G}\) becomes a reduced game \(\mathcal{G}\upharpoonright \sigma ^0_1\) with \(C_2\) as remaining controllable modes. With the significant reduction in size, it may now be feasible to synthesize a near-optimal strategy, \(\sigma ^0_2:\mathbb {C}\rightarrow C_2\), with horizon h for this reduced game, i.e. \(\sigma ^0_2=\text{ argmin }_{\sigma }\mathbb {E}^{\mathcal{G}\upharpoonright \sigma ^0_1,\gamma }_{\sigma ,h}(D)\). Now given \(\sigma ^0_2\), we may similarly learn an optimal sub-strategy, \(\sigma ^1_1:\mathbb {C}\rightarrow C_1\), for the reduced game \(\mathcal{G}\upharpoonright \sigma ^0_2\) with \(C_1\) as remaining controllable modes. Repeating this process will generate a sequence of sub-strategies \(\sigma ^i_1:\mathbb {C}\rightarrow C_1\) and \(\sigma ^i_2:\mathbb {C}\rightarrow C_2\), with \(\sigma ^h_1=\sigma ^N_1\) and \(\sigma ^h_2=\sigma ^N_2\) for some a priori chosen N. Clearly, this method is a heuristic, with no guarantee of converging to the optimum overall strategy, and where the quality depends on the initial sub-strategy chosen, the choice of N as well as the game \(\mathcal{G}\) itself. However, as we shall see, this heuristic may be with success applied to our floor heating case study.
Stratego Online Compositional Controller. (Stratego-ON-CL) This controller applies the previously introduced compositional synthesis together with online synthesis. The controller uses two Uppaal Stratego models. In the first model, valves 1 to 5 are controllable and valves 6 to 11 are fixed by a Bang-Bang controller (the second model is constructed in a dual manner where the valves 1 to 5 are now fixed to the computed control strategy in the first model). At every period, distributing the valve capacity between the left and right parts of the house plays a key role. This controller dynamically assigns the maximum allowed capacity for the two parts of the house proportionally to the distance function dist of the two parts of the house.
Experiments for 11 Rooms. We implemented the floor heating stochastic hybrid game \(\mathcal{G}_{11,8}\) with 11 rooms and 8 doors in our simulator and evaluated the Stratego compositional controller together with the previously defined controllers and all the scenarios described in Sect. 3.1. Table 2 presents the results. We observe that for all scenarios the Stratego online compositional controller obtains results comparable to the Brute-Force controller, however, by an order of magnitude faster.
In order to see how the Stratego-ON-CL controller can take weather information into account, consider Fig. 4 that illustrates the spring stability scenario. From points of time between 0 and 500 min, the outside temperature increases and exceeds the target temperature. We observe that since the Stratego-ON-CL controller is able to look at the weather forecast for the next 45 min, it shuts down the valves much earlier than the other controllers. This results in energy savings and increased comfort.
5 Conclusion
In the floor heating case study we evaluated the existing Uppaal Stratego controller synthesis techniques and showed its limitations when applied on industrial scale models. In order to solve the scalability issues, we proposed online framework to compute and combine the short-term control strategies iteratively on demand, while connected to the real house heating system. In addition, we proposed a compositional methodology in order to scale the synthesis for more rooms needed in our real scenario. The experimental evaluation showed that the resulting strategies are outperforming the presently used controller and while comparable in performance to the Brute-Force controller, our method can compute the control strategy by an order of magnitude faster. Hence the developed framework is suitable for installation at home automation systems and we have already constructed a scaled physical model of the house with the actual hardware used by the company Seluxit, as a first step towards the industrial employment of our methodology in their products.
- 1.
- 2.
[12] won the Embedded Thesis Award 2014 of the Federation of Danish Industry.
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Note that \(\sum _{u'}\int _{\tau }\delta _{(c,u,v)}(\tau ,u') d\tau = 1\) for all (c, u, v).
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Note that there may be several configurations between \(\gamma _{n-1}\) and \(\gamma _n\) due to the environment \(\mathcal{U}\) changing the uncontrollable mode.
The research leading to these results has received funding from the EU FP7 FET projects CASSTING and SENSATION, the project DiCyPS funded by the Innovation Fund Denmark, the Sino Danish Research Center IDEA4CPS and the ERC Advanced Grant LASSO. The fourth author is partially affiliated with FI MU, Brno, Czech Republic.
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Major solar projects change hands in Colorado, California
Source: 8minute
The US renewables business of Japanese oil and gas major Idemitsu Kosan has bought a 100MW solar project in Colorado from GCL New Energy, a subsidiary of China's GCL New Energy Holdings.
Construction of the Pioneer solar project is expected before the close of the year. It is contracted to provide energy to local electric distribution cooperative Intermountain Rural Electric Association through a power purchase agreement (PPA) signed in June.
Solar Frontier America's acquisition of the Pioneer solar [roject is the latest in a string of US activity for the company over the past 12 months. This includes the purchase of the 210MW Mustang Two solar project in California, and the inking of a 15-year agreement to supply power from its 56MW Luciana solar project, also in the sunshine state, to utility East Bay Community Energy.
Charles Pimentel, CEO of Solar Frontier Americas' independent power producer business, said in a release that the latest acquisition was evidence that a "shift is underway with traditional fossil fuel providers to support investments for clean energy that benefit the future of our environment and society."
Allianz turns to 8minute project for first US solar buy
US solar and storage developer 8minute Solar Energy has sold the equity interests of the under-construction 67MW Lotus Solar farm in central California to investment management firm Allianz Global Investors.
The farm, which is contracted to provide energy to utility Southern California Edison through a 20-year PPA, is Allianz' first US solar project. Its annual output is expected to exceed 125 million kWh per year.
In news coinciding with the sale, the project bagged US$140 million of debt financing from NYC-headquartered bank CIT Group and German bank NORD LB. The banks are providing a construction loan, letter of credit and term loan facility for the project.
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US solar prospects amid PPA uptake and a changing policy landscape will take centre stage at Solar Media's Solar & Storage Finance USA, to be held in New York on 29-30 October 2019
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Solar & Storage Finance USA, the only event that connects developers to capital and capital to solar and storage projects, will be back in November 2020 for its 7th edition!
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Also read...
NextEnergy Capital continues investment charge with New York community solar portfolio purchase
COVID-19: Latest analysis on impact to US residential and utility-scale solar
California to add 25GW of renewables by 2030 under new roadmap
COVID-19: SunPower hit by more supply chain issues and major end-market lockdowns
California solar and storage work interpreted as 'essential service' during COVID19


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