Neural Network-Based Event-Triggered Adaptive Control Algorithms for Uncertain Nonlinear Systems with Actuator Failures

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network-based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.