Robust Reinforcement Learning control and its application based on IQC and PSO

摘要：

In this paper a novel robust reinforcement learning control based on IQC(Integral quadratic constraints) and PS0(RRLCIP)is presented．the RRLCIP utilizes a adaptive critic to estimate the decoupling performance．a neural network to generate the decoupling action．and a PI controller to control the plant after decoupling．By replacing nonHnear and time-varying aspects of a neural network with uncertainties，a robust reinforcement learning procedure results that is guaranteed to remain stable even as the neural network is being trained and solve the local minima problem， by making use of the global optimization capability of PSO． performance can be improved through the use of learning．The RRLCIP utilize a plant model to accelerate the convergence speed．Proposed RRI—CIP control strategy can not only find the good performance．but also avoid of unstable behaviOr at learning．The simulation results for control s37stem of collector gas pressure of coke ovens shows its validity．

关键词： robust reinforcement learning control IQC PSO control system of collector gas pressure

作者: Qin bin Li Pingchuan Wang Xin Wang Zebin

作者单位: School of Electrical & Information．Engineering Hunan University of Technology Zhuzhou．China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 505-508

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）