基于SIMCDE-RBF算法的PID参数整定研究

IF 0.5 Q4 AUTOMATION & CONTROL SYSTEMS
Yueting Liu,  Weihua Meng
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引用次数: 0

摘要

针对温控系统的大延迟特性,提出了一种基于存储机制差分进化算法(SIMCDE)的优劣变异交叉策略径向基函数(RBF)方法对PID控制器进行整定和优化。差分进化算法引入了带有存储机制的优劣突变策略和优劣交叉策略,有效地避免了局部最优解。然后,利用SIMCDE算法对RBF的初始参数进行优化,通过RBF在线辨识得到梯度信息;最后根据梯度信息在线调整PID的三个参数。通过对四个测试函数的求解,表明SIMCDE-RBF算法具有良好的优化能力。通过SIMCDE-RBF算法整定PID参数的仿真实验和某乳业公司加热炉温度控制试验表明,与IDE-RBF-PID、GODE-RBF-PID和mcode - rbf -PID相比,SIMCDE-RBF-PID具有更好的动态性能、更强的抗干扰性能和更高的控制精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Research on PID Parameter Tuning Based on SIMCDE-RBF Algorithm

Research on PID Parameter Tuning Based on SIMCDE-RBF Algorithm

Aiming at large delay characteristics of the temperature control system, a radial basis function (RBF) method of superior and inferior mutation crossover strategies with storage mechanism differential evolution algorithm (SIMCDE) is proposed to tune and optimize the PID controller. The differential evolution algorithm introduces superior and inferior mutation strategies with storage mechanisms and superior and inferior crossover strategies, effectively avoiding the local optimal solutions. Then, the SIMCDE algorithm optimizes the initial parameters of RBF, and the gradient information is obtained by RBF online identification. Finally, three parameters of PID are adjusted online according to gradient information. Solving four test functions shows that the SIMCDE-RBF algorithm has good optimization ability. The simulation experiment of SIMCDE-RBF algorithm tuning PID parameters and the test of temperature control of heating furnace in a dairy company show that compared with IDE-RBF-PID, GODE-RBF-PID, and MCOBDE-RBF-PID, SIMCDE-RBF-PID has better dynamic performance, more robust anti-interference performance, and higher control accuracy.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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