A newapproach to robust and optimal nonlinear control design

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new approach to the robust optimal control of nonlinear systems with parametric and dynamic uncertainties. The proposed method is novel and significant in several aspects. First, by means of techniques from reinforcement learning and approximate/adaptive dynamic programming, we bypass the difficulty of solving exactly the Hamilton-Jacobi-Bellman (HJB) equation for nonlinear systems. Instead, a recursive learning scheme known as policy iteration is introduced and its convergence is examined in great details. Second, this paper proposes the first solution to computational optimal nonlinear control in the presence of parametric and dynamic uncertainties. Robustness to dynamic uncertainty is systematically studied using nonlinear small-gain theorems appearing in the work of one of the authors. Finally, the main results are supported by rigorous stability analysis and validated by a practical application to a one-machine power system. It is important to notice that the proposed methodology is general and has a potential impact in other fields such as smart electric grid and systems neuroscience.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013
Pages144-151
Number of pages8
DOIs
StatePublished - 2013
Event3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013 - Phuket, Thailand
Duration: Apr 10 2013Apr 12 2013

Other

Other3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013
CountryThailand
CityPhuket
Period4/10/134/12/13

Fingerprint

Control Design
Uncertainty
Nonlinear systems
Nonlinear Systems
Small Gain Theorem
Policy Iteration
Nonlinear Optimal Control
Adaptive Dynamics
Hamilton-Jacobi-Bellman Equation
Neuroscience
Reinforcement learning
Reinforcement Learning
Robust Control
Dynamic programming
Power System
Dynamic Programming
Stability Analysis
Optimal Control
Robustness
Grid

Keywords

  • Adaptive dynamic programming
  • Input-to-state stability
  • Nonlinear systems
  • Optimal control
  • Small-gain theorem

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Modeling and Simulation

Cite this

Jiang, Z-P., & Jiang, Y. (2013). A newapproach to robust and optimal nonlinear control design. In Proceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013 (pp. 144-151) https://doi.org/10.2316/P.2013.799-029

A newapproach to robust and optimal nonlinear control design. / Jiang, Zhong-Ping; Jiang, Yu.

Proceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013. 2013. p. 144-151.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jiang, Z-P & Jiang, Y 2013, A newapproach to robust and optimal nonlinear control design. in Proceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013. pp. 144-151, 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013, Phuket, Thailand, 4/10/13. https://doi.org/10.2316/P.2013.799-029
Jiang Z-P, Jiang Y. A newapproach to robust and optimal nonlinear control design. In Proceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013. 2013. p. 144-151 https://doi.org/10.2316/P.2013.799-029
Jiang, Zhong-Ping ; Jiang, Yu. / A newapproach to robust and optimal nonlinear control design. Proceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013. 2013. pp. 144-151
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