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In this paper, a fault-tolerant fuzzy model-predictive control with the integral action method for a class of nonlinear uncertain systems is proposed. Nonlinear uncertain systems subject to actuators and/or sensors faults are represented by the Takagi–Sugeno (T-S) fuzzy model. The objective is to design a stable, robust and efficient fault-tolerant controller based on a T-S fuzzy observer with measurable premise variables. The proposed T-S fuzzy observer estimates state vector and faults. Based on Lyapunov theory, the trajectory tracking performances and the closed-loop system stability are analysed. The gains of the fuzzy observer and the pre-stabilized control law are obtained by solving linear matrix inequalities. Simulation results illustrate the robustness of the proposed controller with respect to uncertainties on an academic mathematical system.

In this paper, a Fuzzy-based Model Predictive Control (FMPC) for a class of nonlinear systems subject to faults and uncertainties is proposed. Our contribution comes from the combination of Parallel Distributed Compensation (PDC) and Model Predictive Control (MPC) for nonlinear systems described by Takagi-Sugeno (T-S) fuzzy model. The objective is to design a stable, robust and efficient Fault Tolerant Controller (FTC). The validity of the proposed FTC strategy and its application to faults tolerance is illustrated through an application to a given automotive Cyber-Physical System (CPS).

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.

International audience

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.

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.