Optimal output regulation for a class of uncertain nonlinear systems based on reinforcement learning

QI Jia-xin; MENG Gui-zhi

doi:10.7641/CTA.2024.30806

Abstract：For the optimal output regulation problem of a class of nonlinear systems with unknown nonlinear functions and external disturbances driven by a linear neutral stabilized external system,an adaptive optimal control strategy based on the evaluation executive network algorithm in reinforcement learning and the backstepping method is proposed.First,according to the condition that the regulator equations are solvable and the coordinate transformation,the output regulation problem of uncertain nonlinear systems is transformed into a stabilization problem.A neural network adaptive observer is designed to estimate the unmeasured state by using a radial basis function neural network to approach an unknown nonlinear function.Then an adaptive internal model based on reinforcement learning is designed,and the cost function associated with the internal model is presented,and an approximate optimal algorithm based on the evaluation executive network is used in every step of the backstepping method,all virtual controllers are guaranteed to be optimal.At the same time,the complexity explosion problem in backstepping is avoided by incorporating the dynamic surface technique.Finally,the proposed value function is not only optimized by the optimal adaptive output feedback controller,but also the signal semi globally of the closed loop system is eventually uniformly bounded and the tracking error is within the desired arbitrary accuracy.Numerical simulations verify the effectiveness of the proposed method.