针对使用混合PSO DQN强化学习的非线性过程的分数顺序PID控制器的自适应调
Reza Shahouni1, Masoud Bahraini2, Moslem Abrofarakh3
1School of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran. reza_shahouni@alumni.iust.ac.ir.
Scientific reports
|November 4, 2025
概括
本研究介绍了一种适应的分数顺序PID (FOPID) 调整方法,使用混合粒子群优化 (PSO) 和深度Q网络 (DQN) 强化学习 (RL) 用于海水淡化厂. 这种新的方法提高了动态非线性系统的控制精度和适应性.
科学领域:
- 控制系统工程 控制系统工程
- 在工程领域的人工智能.
- 水处理技术水处理技术
背景情况:
- 淡化厂需要精确控制效率.
- 具有时间延迟和干扰的非线性系统带来了重大的控制挑战.
- 现有的PID调方法往往缺乏适应性和稳定性.
研究的目的:
- 开发一种创新的自适应非线性分数顺序PID (FOPID) 调方法.
- 为了优化流量计控制器在海水淡化厂的控制.
- 在动态运行条件下提高系统性能.
主要方法:
- 集成粒子优化 (PSO) 和基于深度Q网络 (DQN) 的强化学习 (RL).
- 实现适应性控制的动态权重机制.
- 利用分数顺序参数进行全球优化和实时适应性.
主要成果:
- 与传统方法 (GA,FLC) 和先进技术 (NN-PID,PSO) 相比,性能优越.
- 证明了更快的响应时间,减少过度冲击和最小的稳定状态错误.
- 实现了超越标准PSO的增强强性和动态参数演变.
结论:
- 混合PSO-DQN-RL FOPID调方法提供了一个科学新且有效的方法.
- 该方法为带有干扰的非线性系统中的自适应控制设定了基准.
- 由于线下-线上平衡和实时GUI,可扩展的部署是可行的.
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