Event-triggered-based output-feedback control for uncertain nonlinear discrete-time systems: a reinforcement learning method

This paper develops a novel event-triggered (ET) output-feedback control algorithm that utilizes the reinforcement learning (RL) method for uncertain nonlinear discrete-time (DT) systems with unknown control directions. Unlike existing approaches, this work addresses the challenges of a nonstrict-feedback system. To estimate unmeasured system states, a radial basis function neural networks (RBF NNs) based observer is established. An efficient ET mechanism is then proposed to reduce communication redundancy. To tackle the issue of unknown control directions, the ET-based DT Nussbaum gain is employed. Under the Lyapunov stability theorem, it is demonstrated that semi-global ultimate uniform boundedness (SGUUB) is achieved for tracking errors and all signals in closed-loop systems. Simulations are provided to illustrate the effectiveness of the algorithm in handling nonlinear DT systems with unknown control directions.