Event-triggered adaptive output-feedback control for nonlinear state-constrained systems using tangent-type nonlinear mapping

This paper investigates the problem of event-triggered adaptive output-feedback control for multi-input and multi-output (MIMO) uncertain nonlinear systems with time-varying full state constraints. A tangent-type nonlinear mapping function is proposed to transform the state-constrained system into a new one free of constraints, such that the time-varying full state constraints are never violated. Radial basis function neural networks are introduced to compensate for the unknown functions. A state observer is established to estimate the unmeasured states. A suitable event-triggering rule is presented to determine when to transmit control laws. Through Lyapunov analyses, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and time-varying full state constraints are never violated. Finally, simulations are presented to evaluate the efficacy of the proposed approach.