Robust predictive fault-tolerant control based on signal compensation for nonlinear industrial processes with partial actuator failures

In response to the limitations of modern industrial processes in terms of control accuracy and safety, this paper proposes a robust predictive fault-tolerant control method based on signal compensation. This method is generally applicable to nonlinear industrial processes with uncertainty and partial actuator failures. Firstly, a novel model is established for nonlinear industrial processes which includes fault factors and unknown lumped dynamic nonlinear terms. On this basis, sub-compensators are designed to eliminate the influence of unmodeled dynamics on the control effect. This method effectively reduces the conservatism of traditional fault-tolerant control and improves the tracking performance of the control system. In addition, this article provides a detailed demonstration of the stability and convergence of the closed-loop system, and verifies the effectiveness of the proposed method through comparative experiments. The experimental results demonstrate that the proposed method exhibits a excellent control performance in comparison to several mainstream methods.