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    王谦, 孙京诰. 基于改进粒子群优化算法的闭环时滞系统辨识[J]. 华东理工大学学报(自然科学版), 2015, (2): 159-165.
    引用本文: 王谦, 孙京诰. 基于改进粒子群优化算法的闭环时滞系统辨识[J]. 华东理工大学学报(自然科学版), 2015, (2): 159-165.
    WANG Qian, SUN Jing-gao. Closed Loop System Identification with Time Delay Based on Improved Particle Swarm Optimization Algorithm[J]. Journal of East China University of Science and Technology, 2015, (2): 159-165.
    Citation: WANG Qian, SUN Jing-gao. Closed Loop System Identification with Time Delay Based on Improved Particle Swarm Optimization Algorithm[J]. Journal of East China University of Science and Technology, 2015, (2): 159-165.

    基于改进粒子群优化算法的闭环时滞系统辨识

    Closed Loop System Identification with Time Delay Based on Improved Particle Swarm Optimization Algorithm

    • 摘要: 闭环时滞模型参数的辨识一直是先进工业控制领域的一个重要课题。然而由于时滞的存在,被控量不能及时地反映系统所承受的扰动,从而产生明显的超调,使得控制系统的稳定性变差。本文充分利用粒子群优化算法收敛速度较快和混沌运动遍历性的优点,提出了一种基于混沌优化思想的混沌粒子群优化算法来直接辨识含有滞后环节的被控对象的闭环传递函数,而不用将其转化为状态方程。将闭环时滞系统的传递函数通过z变换转化为离散的差分方程,对于滞后环节的处理,用一阶Pade近似。利用CPSO的全局优化能力来极小化误差准则函数,从而获得模型参数的估计值。仿真实验结果证明:该方法收敛速度较快、辨识得到的参数精度较高,适用于实际的工业生产。该方法与辅助变量最小二乘方法相比,计算量小、过程简单、不用计算多重积分、辨识速度较快、辨识精度高。

       

      Abstract: The identification on parameters of closed loop time delay systems has been an important topic in advanced industrial control field. However, due to the existence of time delays, the controlled variable cannot timely reflect the external disturbance, which may result in obvious overshoot and poor stability. Using the advantages of particle swarm optimization algorithm and chaotic motion, this paper proposes a direct identification method on the transfer function of closed loop time delay systems, rather than transforming the transfer function to the state equation. Using z transform, the transfer function of closed loop time delay system is changed into discrete differential equation. Besides, the time delay is approximated by means of first order Pade approximation. CPSO method is adopted to minimize the error criterion function so as to obtain the estimation of the model parameters. It is shown from the simulation results that the proposed method has fast convergence speed and higher identification accuracy.

       

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