In this article, to address tracking problems in nonlinear industrial systems with time-delays, partial actuator faults, model uncertainties, and external unknown disturbances, a method known as Fault-tolerant Robust Model Predictive Control (FTRMPC) is proposed. First, an infinite horizon cost function is meticulously designed and a Lyapunov-Krasovskii function (LKF) associated with delays is established. The upper bound of the infinite horizon cost function is determined by using the Linear Matrix Inequalities (LMI) technology, and it is minimized at each step to obtain the corresponding FTRMPC state feedback control sequences. Then the Lyapunov-Razumikhin function (LRF) is employed to ensures that the system states keeps within the Robust Positively Invariant (RPI) set while simultaneously satisfying the control law. Finally, the optimal controller reconfiguration mechanism is utilized to adjust the parameters of the model-based predictive controller, thereby achieving system fault tolerance and restoring system performance. To further verify the effectiveness of the proposed method, various simulation experiments are designed to comprehensively assess its performance, thus demonstrating its applicability in complex environments.
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