Quasi-LPV model predictive reconfigurable control for constrained nonlinear systems

This paper deals with the control laws reconfiguration of nonlinear systems, by using a Fuzzy-Model-based Predictive Control (FMPC). It should be noted that the studied systems are written in the quasi-linear parametric varying (quasi-LPV) form. This FMPC strategy is developed to preserve closed-loop stability in the nominal and actuator faulty case. Fault accommodation by perturbations rejection is presented. This step is done by interpolation-based control to cover the entire area of operation. To allow the process to maintain current performances closed to desired performances, a dynamic optimizer is used. Our contribution comes from the combination of several aspects: fuzzy model, quadratic programming and faults decoupling principle. The linearization around a family of equilibrium points is also studied. The operating points are appropriately configured by a set of variables called premise.