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Event-trigger-based Neural Network Controller for Pure-feedback Nonlinear Systems with Full-state Constraints

Yang Gao, Zhongcai Zhang*, and Linran Tian
International Journal of Control, Automation, and Systems, vol. 20, no. 4, pp.1226-1237, 2022

Abstract : In this paper, we study the event-triggered output tracking control problem for a class of pure-feedback nonlinear systems subject to asymmetric time-varying full-state constraints, lumped disturbances and uncertainties. By introducing a state-dependent function, the original constrained system is transformed into a new system which is completely equivalent to the former. Then, an event-triggered adaptive neural network (NN) controller is developed to stabilize the new system. The problems of circular design and feasibility conditions are circumvented by coordinate transformation technique based on the dynamic surface control (DSC) simultaneously. It has been shown that the output tracking error can converge to an arbitrary predefined compact set under the proposed method. In addition, the boundedness of all signals is ensured through rigorous proof. And meanwhile, it is demonstrated that the designed controller has the ability to maintain the desired state constraints under the lumped disturbances and uncertainties, and reduce the burden of communication transmission. Finally, the simulation results show the effectiveness of the control algorithm.

Keyword : Disturbances barrier Lyapunov function, event-triggered control, neural network, pure-feedback systems, state constraints.

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