Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 65, Issue 1, Pages 654-663Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2722424
Keywords
Adaptive critic design; neural networks; optimal control; robust trajectory tracking; self-learning control; system uncertainty
Categories
Funding
- Beijing Natural Science Foundation [4162065]
- National Natural Science Foundation of China [U1501251]
- Tianjin Natural Science Foundation [14JCQNJC05400]
- State Key Laboratory of Management and Control for Complex Systems [20170105]
- China Postdoctoral Science Foundation [2014M561559]
- SKLMCCS
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In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton-Jacobi-Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring-mass-damper system is included.
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