| 一类非线性离散系统自适应准滑模控制 |
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| 引用本文： | 侯忠生,王卫红,金尚泰. 一类非线性离散系统自适应准滑模控制[J]. 控制理论与应用, 2009, 26(5): 505-509 |
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| 作者姓名： | 侯忠生 王卫红 金尚泰 |
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| 作者单位： | 1. 北京交通大学,电子与信息工程学院,先进控制系统研究所,北京,100044
2. 北京交通大学,电子与信息工程学院,先进控制系统研究所,北京,100044;太原科技大学,自动化系,山西,太原,030024 |
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| 基金项目： | 国家自然科学基金资助项目(60774022); 国家自然科学基金重点资助项目(60834001). |
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| 摘 要： | 针对一般非线性离散时间系统的不确定性和扰动抑制问题, 提出一种新的自适应准滑模控制算法. 算法包括两部分, 其一是基于紧格式动态线性化模型的自适应准滑模控制器设计, 其中动态线性化方法中“伪偏导数”的估计算法仅依赖于系统I/O 实时量测值. 其二是采用径向基神经网络估计器来估计系统的综合不确定性. 理论分析证明了系统的BIBO稳定性. 仿真结果验证了所提算法的有效性.
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| 关 键 词： | 准滑模控制 无模型自适应控制 动态线性化 神经网络 非线性离散时间系统 |
| 收稿时间： | 2008-05-06 |
| 修稿时间： | 2008-08-27 |
Adaptive quasi-sliding-mode control for a class of nonlinear discrete-time systems |
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| HOU Zhong-sheng,WANG Wei-hong and JIN Shang-tai. Adaptive quasi-sliding-mode control for a class of nonlinear discrete-time systems[J]. Control Theory & Applications, 2009, 26(5): 505-509 |
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| Authors: | HOU Zhong-sheng WANG Wei-hong JIN Shang-tai |
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| Affiliation: | Advanced Control Systems Laboratory,School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;Advanced Control Systems Laboratory,School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; Department of Automation, Taiyuan University of Science & Technology, Taiyuan Shanxi 030024, China;Advanced Control Systems Laboratory,School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | A new adaptive quasi-sliding-mode control algorithm is proposed to deal with the problems of disturbances and uncertainty in general nonlinear discrete-time systems. The algorithm includes two parts: one is the design of an adaptive quasi-sliding-mode controller based on the tight-format dynamic linearization model, whose linearization parameters, i.e. pseudo-partial derivatives(PPD) are estimated on-line from the I/O(input/output) information of the system; the other is the estimation of the system uncertain part by employing a RBFNN(radical base function neural network)-based predictor. The BIBO(bounded-input bounded-output) stability is also proved through rigorous theoretical analysis. Finally, the simulation results validate the effectiveness of the proposed method. |
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| Keywords: | quasi-sliding mode control model-free adaptive control dynamic linearization neural network nonlinear discrete-time systems |
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