Event-Based Adaptive Neural Network Control for Large-Scale Systems With Nonconstant Control Gains and Unknown Measurement Sensitivity

This study explored the issue of decentralized adaptive event-triggered neural network (NN) control for nonlinear interconnected large-scale systems (LSSs) subjected to unknown measurement sensitivity and nonconstant control gains. Due to the impact of unknown measurement sensitivity, the real states of LSSs cannot be directly utilized. To overcome this difficulty, an effective adaptive feedback control scheme was developed. Subsequently, NNs were exploited to address the nonlinear terms and unknown nonconstant control gains. A modified first-order compensation system was developed to enhance the control performance in the presence of saturation nonlinearity. Furthermore, a significant dynamic event-triggered control (DETC) protocol was developed based on the saturation controller and measurement error, which reduced the number of controller updates. According to the Lyapunov stability theory, the proposed DETC-based decentralized adaptive protocol demonstrated that all signals were semiglobally uniformly ultimately bounded. The simulation examples illustrate the validity of the presented control protocol.