Echo state network-based online optimal control for discrete-time nonlinear systems

作者：

Highlights：

• This article develops an online ADP method for discrete nonlinear systems, it can reduce the computation burden.

• Two ESNs are employed to implement the online learning and the output weights are obtained simultaneously.

• The hidden layer of ESN is generated randomly rather than designed with much effort, which reduces the application complexity.

摘要

•This article develops an online ADP method for discrete nonlinear systems, it can reduce the computation burden.•Two ESNs are employed to implement the online learning and the output weights are obtained simultaneously.•The hidden layer of ESN is generated randomly rather than designed with much effort, which reduces the application complexity.

论文关键词：Optimal control,Discrete-time systems,Adaptive dynamic programming (ADP),Echo state network (ESN)

论文评审过程：Received 16 December 2020, Revised 10 March 2021, Accepted 13 April 2021, Available online 1 June 2021, Version of Record 1 June 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126324