Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems

This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dy...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 25; no. 5; pp. 882 - 893
Main Authors: Jiang, Yu, Jiang, Zhong-Ping
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptative systems
Adaptive dynamic programming (ADP)
Applied sciences
Approximation methods
Artificial intelligence
Closed loop systems
Computer science; control theory; systems
Control system synthesis
Control systems
Control theory
Control theory. Systems
Design engineering
Dynamic programming
Dynamical systems
Exact sciences and technology
Learning
Learning and adaptive systems
Nonlinear dynamics
Nonlinear systems
nonlinear uncertain systems
Nonlinearity
Optimal control
robust optimal control
Robustness
Uncertainty
nonlinear uncertain systems
Adaptive dynamic programming (ADP)
robust optimal control
Methodology
Non linear control
Feedback regulation
Control synthesis
Optimization
Optimal design
Uncertain system
Unmodeled dynamics
Dynamic programming
Learning algorithm
Optimal control (mathematics)
Single machine
Adaptive dynamic programming
Turbojet
Reinforcement learning
Jet engine
Non linear system
Recursive algorithm
Backstepping control
Robust control
Optimal control
Small gain theorem
Control theory
Artificial intelligence
Output feedback
Lyapunov function
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed RADP methodology can be viewed as an extension of ADP to uncertain nonlinear systems. Practical learning algorithms are developed in this paper, and have been applied to the controller design problems for a jet engine and a one-machine power system.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2013.2294968