Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview

Weinan Gao, Zhong Ping Jiang

Research output: Contribution to journalReview articlepeer-review


This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection. The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering. Under this framework, one can solve optimal output regulation problems of linear, partially linear, nonlinear, and multi-agent systems in a data-driven manner. We will also review some practical applications based on this framework, such as semi-autonomous vehicles, connected and autonomous vehicles, and nonlinear oscillators.

Original languageEnglish (US)
JournalControl Theory and Technology
Issue number1
StatePublished - Feb 2022


  • Adaptive dynamic programming
  • Adaptive optimal output regulation
  • Learning-based control
  • Reinforcement learning

ASJC Scopus subject areas

  • Control and Optimization
  • Information Systems
  • Aerospace Engineering
  • Signal Processing
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Modeling and Simulation


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