Fuzzy Model Predictive reconfigurable control for nonlinear systems subject to actuators faults

In this paper, a fault tolerant Fuzzy-Model-Predictive Control (FMPC) method for a class of nonlinear systems is proposed. Nonlinear systems subject to actuators faults are described by Takagi-Sugeno (T-S) fuzzy model. The objective of this approach is to design a Fault Tolerant Controller (FTC). At each sampling time, MPC solves an optimization to achieve desired set points and control objectives. The feasibility of optimization problem provides the guarantee of the nominal asymptotic stability. However the optimization can be infeasible due to faults. This motivates the development of methods to recover feasibility without violating constraints imposed on control inputs and system states. The investigation is mainly concerned with robustness of the MPC regarding actuators faults. The proposed FMPC with Unmeasurable Premise Variables (UPV) is compared to classical MPC and PI controller. The effectiveness and good performances of the proposed FTC strategy and its application to faults tolerance is illustrated by an academic example.