Event-Based Adaptive Intelligent Control for LSSs with Time-Varying Output Constraints

This paper explores a decentralized adaptive intelligent control problem for large-scale systems in nonstrict-feedback structure with external disturbances and time-varying output constraints. An effective event-triggered strategy is utilized to reduce the number of messages to be transmitted. Furthermore, an asymmetric time-varying barrier Lyapunov function is adopted to handle the problem of time-varying output constraints of nonlinear LSSs. Using Lyapunov stability theory, an adaptive neural network compensation controller with a first-order sliding-mode differentiator is developed to avoid the issues of unknown disturbances and “explosion of complexity”. The developed control strategy assures that all signals are bounded. Finally, some simulation results indicate the validity of the developed control protocol.