Neural-network-based Adaptive Fault-tolerant Control of Switched Nonlinear Systems with Full State Constraints

This paper studies the adaptive fault-tolerant control problem for switched nonlinear systems with full-state constraints and unknown functions. To address this challenge, radial basis function neural networks (RBFNNs) are utilized for function approximation, barrier Lyapunov functions (BLFs) are applied for ensuring constraint adherence, and the backstepping technique is employed for systematical control design. The combination of these methods is relatively novel, and on this basis, actuator faults are compensated. It is demonstrated that all closed-loop signals are globally bounded with the tracking error confined within a predefined compact set.