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一类非线性离散系统自适应准滑模控制
  • 期刊名称：控制理论与应用, vol. 26, no. 5, pp. 505-509, 2009.
  • 时间：0
  • 分类：TP273[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
  • 作者机构：[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]太原科技大学自动化系,山西太原030024
  • 相关基金：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）.
  • 相关项目：高阶内模学习控制理论及在快速路交通控制中的应用
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, quasi-sliding mode control, model-free adaptive control, dynamic linearization, neural network, nonlinear discrete-time systems.
中文摘要：

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．

英文摘要：

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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