Please use this identifier to cite or link to this item:
Title: Adaptive near-optimal control of uncertain systems with application to underactuated surface vessels
Authors: Zhang, Y
Li, S 
Liu, X
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on control systems technology, 2017 (article in press) How to cite?
Journal: IEEE transactions on control systems technology 
Abstract: In this paper, a unified online adaptive near-optimal control framework is proposed for linear and nonlinear systems with parameter uncertainty. Under this framework, auxiliary systems converging to the unknown dynamics are constructed to approximate and compensate the parameter uncertainty. With the aid of the auxiliary system, future outputs of the controlled system are predicted recursively. By utilizing a predictive time-scale approximation technique, the nonlinear dynamic programming problem for optimal control is significantly simplified and decoupled from the parameter learning dynamics: the finite-horizon integral-type objective function is simplified into a quadratic one relative to the control action and there is no need to solve time-consuming Hamilton equations. Theoretical analysis shows that closed-loop systems are asymptotically stable. It is also proved that the proposed adaptive near-optimal control law asymptotically converges to the optimal. The efficacy of the proposed framework and the theoretical results are validated by an application to underactuated surface vessels.
ISSN: 1063-6536
EISSN: 1558-0865
DOI: 10.1109/TCST.2017.2705057
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Citations as of Jun 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.