| Neural model-based adaptive control for systems with unknown Preisach-type hysteresis |
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| 作者姓名： | Chuntao LI Yonghong TAN |
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| 作者单位： | [1]CollegeofAutomation,NanjingUniversityofAeronauticsandAstronautics,NanjingJiangsu210016,China [2]LabofIntelligentSystemsandControlEngineering,GuilinUniversityofElectronicTechnology,GuilinGuangxi541000,China |
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| 基金项目： | This work was partially supported by National Science Foundation of China(No.50265001)，Guangxi Science Foundation(No.0339068). |
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| 摘 要： | An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.
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| 关 键 词： | 自适应控制 神经网络 磁滞现象 SISO |
| 收稿时间： | 9 November 2003 |
| 修稿时间： | 2004/8/18 0:00:00 |
Neural model-based adaptive control for systems with unknown Preisach-type hysteresis |
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| Chuntao LI,Yonghong TAN.Neural model-based adaptive control for systems with unknown Preisach-type hysteresis[J].Journal of Control Theory and Applications,2004,2(1):51-59. |
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| Authors: | Chuntao LI Yonghong TAN |
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| Affiliation: | 1. College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China
2. Lab of Intelligent Systems and Control Engineering, Guilin University of Electronic Technology, Guilin Guangxi 541000, China |
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| Abstract: | An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firsdy developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The kws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semi- global stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated. |
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| Keywords: | Neural networks Hysteresis Adaptive control Preisach model |
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