Reinforcement learning output feedback NN control using deterministic learning technique

Bin Xu, Chenguang Yang, Zhongke Shi

Research output: Contribution to journalArticlepeer-review

247 Scopus citations

Abstract

In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control.

Original languageEnglish
Article number6681972
Pages (from-to)635-641
Number of pages7
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number3
DOIs
StatePublished - Mar 2014

Keywords

  • Approximate dynamic programming
  • discrete-time system
  • output feedback control
  • pure-feedback system
  • radial basis function neural network (RBF NN)

Fingerprint

Dive into the research topics of 'Reinforcement learning output feedback NN control using deterministic learning technique'. Together they form a unique fingerprint.

Cite this