期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 40, 期 3, 页码 915-927出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2009.2033563
关键词
Adaptive control; dynamic surface control (DSC); minimal learning parameter (MLP); neural control; nonlinear systems
类别
资金
- Hong Kong Research Grant Council [CityU 113708]
- National Natural Science Foundation of China [50640460116, 60874056, 60674037, 60674056, 50779033]
- China Postdoctoral Science Foundation [20070420101]
- Shanghai Postdoctoral Scientific Program [07R214128]
- National High Technology Research and Development (863 Plan) Program of China [2007AA11Z250]
A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining dynamic surface control and minimal learning parameter techniques. The key features of the algorithm are that, first, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.
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