Improvement of predictive control algorithm based on fuzzy fractional order PID

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rongzhen Shi
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引用次数: 0

Abstract

Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.
基于模糊分数阶PID的预测控制算法改进
现有的预测控制策略对被控过程具有全面的先验知识,对参数优化搜索空间的连续性要求较弱,在一定程度上限制了其应用。因此,对模糊分数阶比例积分微分(PID)预测控制算法进行了改进研究。首先,提出了PID预测控制装置的控制原理。根据这一原理,构造了PID预测控制设备的自适应模糊PID节能控制器结构。通过PID节能控制参数整定原理和模糊控制规则,实现了PID预测控制设备的自适应模糊PID节能控制。最后,构建分数阶PID预测传递函数模型,对基于PID优化技术的预测控制算法进行改进。实验结果表明,所设计算法的精度和效率均能获得最佳性能指标,其稳定性、超调量、时间、控制精度基本不变。在小区域温度控制中,干扰干扰小,抗干扰能力好,具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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