期刊
IET CONTROL THEORY AND APPLICATIONS
卷 5, 期 16, 页码 1868-1880出版社
WILEY
DOI: 10.1049/iet-cta.2010.0711
关键词
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资金
- National Natural Science Funds of China [60874044, 91016006]
- Research Foundation for Key Disciplines of Beijing Municipal Commission of Education [XK100060422]
In this study, an adaptive neural network dynamic surface control for a class of pure-feedback non-linear systems with dynamic uncertainties and unknown hysteresis is proposed. The hysteresis is described by a saturated-type Prandtl-Ishlinskii (PI) model, which is more applicable than the traditional PI model. The main advantages of the authors scheme are that by introducing an initialising technique, the L-infinity performance of the tracking error can be achieved, the assumption on dynamic uncertainties is relaxed, the explosion of the complexity problem when the hysteresis is fused with back-stepping design can be eliminated, which together with the estimation of vector norm of unknown parameters makes the control law be simplified and the computational burden be greatly reduced. Simulation results are presented to demonstrate the efficiency of the proposed scheme.
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