Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 26, Issue 6, Pages 1188-1201Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2014.2334638
Keywords
Adaptive output feedback control; neural network (NN); stochastic nonlinear systems; time-varying delays; unknown control directions
Categories
Funding
- National Natural Science Foundation of China [51179019, 61374114, 61273137]
- Program for Liaoning Excellent Talents in University [LR2012016]
- Applied Basic Research Program, Ministry of Transport of China [2011-329-225-390, 2013-329-225-270]
- Fundamental Research Funds for the Central Universities [313-2014-321]
- National Fundamental Research 973 Program of China [2011CB302801]
- Macau Science and Technology Development Foundation, Macau, China [008/2010/A1]
- Multiyear Research Grants
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This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficients are grouped together and the original system is transformed into a new system using a linear state transformation technique. Then, the Nussbaum function technique is incorporated into the backstepping recursive design technique to solve the problem of unknown control directions. Furthermore, under the assumption that the time-varying delays exist in the system output, only one NN is employed to compensate for all unknown nonlinear terms depending on the delayed output. Moreover, by estimating the maximum of NN parameters instead of the parameters themselves, the NN parameters to be estimated are greatly decreased and the online learning time is also dramatically decreased. It is shown that all the signals of the closed-loop system are bounded in probability. The effectiveness of the proposed scheme is demonstrated by the simulation results.
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