Guest Editorial for Special Issue on Extensions of Reinforcement Learning and Adaptive Control

ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for years in process control,industry,aerospace systems。

Asymptotic tracking by a reinforcement learning-based adaptive critic controller

Adaptive critic(AC) based controllers are typically discrete and/or yield a uniformly ultimately bounded stability result because of the presence of disturbances and unknown approximation errors.A continuous-time AC controller is developed that yields asymptotic tracking of a class of uncertain nonlinear systems with bounded disturbances.The proposed AC-based controller consists of two neural networks(NNs)-an action NN,also called the actor,which approximates the plant dynamics and generates appropriate control actions;and a critic NN,which evaluates the performance of the actor based on some performance index.The reinforcement signal from the critic is used to develop a composite weight tuning law for the action NN based on Lyapunov stability analysis.A recently developed robust feedback technique,robust integral of the sign of the error(RISE),is used in conjunction with the feedforward action neural network to yield a semiglobal asymptotic result.Experimental results are provided that illustrate the performance of the developed controller.