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arXiv 1202.5686eess.SYcs.SY
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基于遗传算法的奈奎斯特平面改进次优模型降阶用于通过遗传编程提取PID和PIλDμ控制器的最优整定规则

Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PIλDμ Controllers via Genetic Programming

Jadavpur University(贾达普大学);Bhabha Atomic Research Centre(巴哈原子研究中心)

Saptarshi Das, Indranil Pan, Shantanu Das, Amitava Gupta

AI总结:

本文利用遗传算法进行奈奎斯特平面上的次优模型降阶,并基于遗传编程从高阶过程测试平台提取PID和分数阶PIλDμ控制器的最优时域整定规则。

摘要:

本文采用遗传算法(GA)进行一种新的基于奈奎斯特的次优模型降阶以及PID和分数阶(FO)PIλDμ控制器的最优时域整定。比较研究表明,新的模型降阶技术优于传统的基于H2范数的降阶建模技术。接下来,通过遗传编程(GP)在加权积分误差指标和控制信号最小化的条件下,利用高阶过程测试平台开发了最优整定规则。从帕累托最优前沿(即公式复杂性与控制性能之间的权衡)出发,为时域最优PID和PIλDμ控制器生成了一组高效的整定规则。

英文摘要:

Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PIλDμ controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PIλDμ controllers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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