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Event-Triggered Anti-disturbance Tracking Control of non-Gaussian Stochastic Distribution Systems with Exogenous Disturbances

This paper presents an anti-disturbance PI control scheme for non-Gaussian stochastic systems subject to exogenous disturbances under an event-triggered mechanism. The objective is to control the output probability density function (PDF) of the system to track a given objective function. Firstly, the square root B-spline is commonly used as a tool for modelling the system output PDFs, and the PDF tracking control problem can be transformed into a finite dimensional dynamic vector tracking control problem. Secondly, the event-triggered mechanism is introduced into the control system, the disturbance observer based control (DOBC) method is used to estimate the unknown external disturbance and combine the state feedback to design a PI type dynamic tracking controller. Moreover, by designing the solution with a lower bound on the minimum trigger interval for event trigger greater than zero, it ensures that the Zeno phenomenon in the event-triggered mechanism can be avoided. Finally, The analysis of convex optimization theory guarantees the good system stability, target dynamic tracking and robustness of the stochastic distribution system. The effectiveness of the method is verified by simulating the styrene molecular weight distribution (MWD) model.