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计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

非线性PID 自学习控制方法研究

曾喆昭1,贺 莹1,张 畅1,李 霖2   

  1. (1. 长沙理工大学电气与信息工程学院,长沙410004;2. 邵阳供电公司,湖南邵阳422800)
  • 收稿日期:2013-09-10 出版日期:2014-10-15 发布日期:2014-10-13
  • 作者简介:曾喆昭(1963 - ),男,教授、博士,主研方向:智能计算,智能控制;贺 莹、张 畅,硕士研究生;李 霖,硕士。
  • 基金资助:
    湖南省自然科学基金资助项目(11JJ6064);湖南省科技计划基金资助项目(2011GK3122)。

Research on Nonlinear PID Control Method for Self-learning

ZENG Zhe-zhao 1,HE Ying 1,ZHANG Chang 1,LI Lin 2   

  1. (1. College of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410004,China;2. Shaoyang City Power Supply Company,Shaoyang 422800,China)
  • Received:2013-09-10 Online:2014-10-15 Published:2014-10-13

摘要: 针对非线性、多变量、强耦合系统的控制问题,提出一种基于双曲函数的非线性PID 自学习控制方法。在PID 控制过程中,用双曲函数构造比例、积分、微分3 个增益参数分别随误差变化的规律曲线,将传统线性PID 控制律转化为非线性PID 控制律的控制思想,并使用自学习算法实时调整3 个非线性增益函数的增益系数,实现基于双曲函数的非线性PID 自学习控制。仿真实验结果表明,与其他控制方法相比,该方法具有更强的鲁棒稳定性和抗扰动能力,是一有效的控制方法,在非线性控制领域具有重要的应用价值。

关键词: 非线性PID, 双曲函数, 自学习控制, 增益参数, 鲁棒稳定性, 抗扰动能力

Abstract: Aiming at nonlinear,multivariable,strong coupling system control problem,a nonlinear PID control method for self-learning based on hyperbolic function is proposed. Due to the process in PID controlling,it forms a consensus in the control field that the curves of proportional,integral,differential three gain parameters following error changes. Thus, this method uses hyperbolic function to construct the rule curves of PID three gain parameters following error changes, which puts forward a control theory about making the traditional linear PID control law into nonlinear PID control law, and uses self-learning algorithm to adjust the three nonlinear gain function coefficients in real time. The self-learning control on nonlinear PID is implemented based on the hyperbolic function. Compared with other controlling methods,the simulation results show that the approach achieves a better robust stability and anti-disturbance capacity,which is an effective control method and has important application value in the nonlinear control filed.

Key words: nonlinear PID, hyperbolic function, self-learning control, gain parameter, robust stability, anti-disturbance capacity

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