Fault diagnosis and model predictive fault-tolerant control for stochastic distribution collaborative systems based on the T–S fuzzy model

This paper presents a fault-tolerant control scheme for a class of nonlinear stochastic distribution collaborative control systems, which are composed of two nonlinear subsystems connected in series to complete the target. The Takagi–Sugeno (T–S) fuzzy model is applied to approximate the nonlinear dynamics of a subsystem. The output of the whole system is the output probability density function (PDF) of the second subsystem. The fuzzy logic systems (FLS) is used to approximate the output PDF. To diagnose the fault that occurred in the first subsystem, an adaptive diagnostic observer and linear matrix inequality (LMI) technique are used to obtain the adaptive tuning law to estimate the fault. When a fault occurs, the fault itself cannot be compensated in the first subsystem and a model predictive fault-tolerant controller is designed in the second subsystem to compensate the fault, making the post-fault output PDF still track the desired PDF as close as possible. A simulated example is given, and the desired results have been obtained.