Identification and optimal control of nonlinear systems using recurrent neural networks and reinforcement learning:An overview

摘要

This paper reviews the identification and optimal control problems using recurrent neural networks and reinforcement learning for nonlinear systems both in discrete-and continuous-time. Since neural networks can approximate any nonlinear function, then is shown that it can approximate a dynamical system using some well-identified elements and different neural structures. Existing methods using Lyapunov and Riccati equations to get the neural weights update rules are reviewed. Optimal control using a linear quadratic regulator formulation or reinforcement learning methods are discussed. We discuss the normalized gradient descent algorithm as core algorithm for the optimal control design using reinforcement learning.(c) 2021 Elsevier B.V. All rights reserved.