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Robust model predictive control for visual servoing

Visual servoing methods have proven their usefulness for robot control in unstructured environments. However, the practicality of such methods highly depends on their robustness to system uncertainties and ability to handle the constraints of the system. Many of the previous works proposed effective remedies for constraint handling; yet, only a few of them considered the system uncertainties. This work proposes a novel control scheme for visual servoing which basically exploits a model predictive controller to handle the constraints. In addition to that, the uncertainty model of the system is developed to handle the constraints more efficiently. The simulation and experimental results confirm the effectiveness of the proposed control method for constraint handling, in the presence of system uncertainties.