Minimum rational entropy fault-tolerant control for nonlinear stochastic distribution control systems with quantized signals

The fault-tolerant control (FTC) issue for quantized nonlinear stochastic distribution control (SDC) systems in the presence of both actuator and sensor faults is addressed. A two-step fuzzy modeling approach is employed to systematically construct the static and dynamic models of the system, which establishes a foundational framework for subsequent fault diagnosis (FD) and FTC. Building upon the model, an adaptive augmented observer is designed to estimate actuator and sensor faults simultaneously, even under the influence of quantization effects. Furthermore, an innovative comprehensive FTC strategy is proposed, in which a virtual sensor compensator is integrated with a minimum rational entropy (MRE) fault-tolerant controller to effectively compensate for faults and ensure the system stability. The practical effectiveness of the proposed methodology is validated through its application to a molecular weight distribution system.