摘要
针对锅炉过热汽温模型结构和参数发生较大变化时常规PID控制效果难以令人满意的问题,提出一款基于actor-critic(AC)强化学习(reinforcement learning, RL)的自适应PI控制器.控制器采用径向基神经网络(RBF-NN)实现AC强化学习结构,其中actor网络输出为PI控制器参数,cri-tic网络对actor网络输出进行评判以生成时序差分(temporal difference, TD)误差信号,TD误差信号驱动RBF网络权值在线更新.介绍了锅炉过热汽温控制系统结构特点,给出了RL-PI控制器设计和算法执行步骤.完成了锅炉过热汽温控制系统的设计.以典型的非线性时变锅炉过热汽温系统为被控对象,进行了正常工况、增益增大、惯性增大、增益突变、惯性突变以及加扰动等6种工况下的仿真试验.结果表明:与模型预测控制、模糊控制以及常规PI串级控制方法相比,该RL-PI控制器具有明显的优势,能够极大提高系统适应工况变换的能力,且具有更强的自学习能力,收敛速度更快,鲁棒性更强.
To solve the problem that the conventional PID with unsatisfactory control effect when the structure and parameters of the boiler superheated temperature model changed greatly, an adaptive PI controller was proposed based on actor-critical(AC) reinforcement learning(RL). A radial basis function neural network(RBF-NN) was used to realize AC-RL structure. The PI controller parameter was used as output of actor network and evaluated by the critical network to generate temporal difference(TD) error signal. The weights of RBF-NN were constantly updated by the TD error signal online. The structural characteristics of boiler super-heated steam temperature control system were introduced, and the RL-PI controller design and algorithm implementation steps were given to complete the design of boiler super-heated steam temperature control system. The simulations of the typical nonlinear time-varying super-heated steam temperature system were carried out with the proposed adaptive RL-PI controller under six working conditions of normal working conditions, gain increase, inertia increase, gain mutation, inertia mutation and disturbance. The results show that compared with model predictive control, fuzzy control and conventional PI cascade control methods, the proposed RL-PI controller has stronger self-learning ability, faster convergence speed and stronger robustness.
作者
于来宝
谢兴旺
宋晶
袁博
YU Laibao;XIE Xingwang;SONG Jing;YUAN Bo(Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan,Hubei 430074,China;College of Electromechanical Engineering,Wuhan City Polytechnic,Wuhan,Hubei 430070,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan,Hubei 430074,China)
出处
《江苏大学学报（自然科学版）》
CAS
北大核心
2022年第6期685-690,共6页
Journal of Jiangsu University：Natural Science Edition
基金
国家自然科学基金资助项目(51228701)
2022年湖北省教育厅科学技术研究项目
武城职硕博士专项课题项目(2022whcvcB02)。
