Robust Model Predictive Control Design for Fault-Tolerant Control of Process Systems

This work considers the problem of stabilization of nonlinear systems subject to constraints, uncertainty and faults in the control actuator. We first design a robust model predictive controller that allows for an explicit characterization of the set of initial conditions starting from where feasibility of the optimization problem and closed-loop stability is guaranteed. The main idea in designing the robust model predictive controller is to employ Lyapunov-based techniques to formulate constraints that (a) explicitly account for uncertainty in the predictive control law, without making the optimization problem computationally intractable, and (b) allow for explicitly characterizing the set of initial conditions starting from where the constraints are guaranteed to be initially and successively feasible. The explicit characterization of the stability region, together with the constraint handling capabilities and optimality properties of the predictive controller, is utilized to achieve fault-tolerant control of nonlinear systems subject to uncertainty, constraints, and faults in the control actuators. The implementation of the proposed method is illustrated via a chemical reactor example.