Adaptive Neural Fault-Tolerant Observer-Based Stabilization of Uncertain MIMO Time-Delay Nonlinear Systems with Dead-Zones and Faults in Actuators

This chapter addresses the stabilization of a general class of multi-input multi-output (MIMO) nonlinear systems with time-varying time delay, unmeasurable states, and in the presence of dead-zones and faults in actuators. The method deals with asymmetric dead-zones and tolerates faults due to loss of effectiveness and bias. A suitable observer is designed to estimate the system’s unmeasurable states. Furthermore, based on the universal approximation theorem, radial basis function neural networks (RBFNNs) are employed to tackle the unknown nonlinear functions within the system. To overcome the time-varying time delay, the Lyapunov-Krasovskii synthesis approach is used. Thanks to this approach and the interval method, the adaptive laws are derived, and it is proved that all the adaptive parameters remain bounded while the controller avoids singularity issues. It is rigorously shown that all the closed-loop signals remain bounded, and the state estimation errors and actual states converge to zero asymptotically. To show applicability and effectiveness, the proposed method is applied to the unified chaotic system with unknown nonlinear functions, unknown time-varying time delay, unmeasurable states, and unknown dead-zones and faults in actuators.