Event‐triggered adaptive neural asymptotic tracking of uncertain constrained nonlinear systems without feasibility condition

Abstract

In this article, an event triggered control problem is investigated for uncertain nonlinear systems with full state constraints. First, the original-constrained system is converted into an equivalent totally unconstrained one by using a state dependent function. Then, an event-triggered adaptive neural-network controller is designed to reduce the communication burden. In addition, rigorous theoretical analyses have been conducted to show that the proposed controller can ensure that all signals in the closed-loop control system are bounded and that the asymptotic convergence with zero tracking error is achieved. In the meanwhile, the state constrains are not violated. Finally, the effectiveness of the presented control scheme is verified through the simulation results.

Event‐triggered adaptive fuzzy optimal control of modular robot manipulators using zero‐sum differential game through value iteration

“…For the past few years, event‐triggered algorithm ADP‐based control has grown into a feasible method to solve optimal control 28‐30 . An event‐triggered control is proposed to deal with the control problems of uncertain nonlinear systems in Reference 31.…”