基于PILCO架构的自适应内模控制器优化  

An Adaptive Internal Model Optimization Based on PILCO Architecture

作　　者：梁俊朗 高健[1] LIANG Junlang;GAO Jian(Key Laboratory of Microelectronic Precision Manufacturing Technology and Equipment Education of Ministry of Education,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学省部共建精密电子制造技术与装备国家重点实验室,广州510006

出　　处：《组合机床与自动化加工技术》2023年第9期59-62,67,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(52075106,U20A6004)。

摘　　要：PID控制器广泛运用于精密控制领域,但其参数的自适应精准调节还存在一定的难度。针对直线电机驱动的精密运动平台,通过构造内模控制器设计来降低PID参数调节难度,为建立内模控制器所需的精确数学模型,在强化学习方法研究的基础上,提出一种基于概率推理学习控制算法(probabilistic inference for learning control, PILCO)的自适应内模滤波系数在线优化方法,根据输入滤波系数与输出误差拟合概率动力学模型,通过策略评估和策略优化进一步优化内模滤波系数。所提的内模控制方法在直线电机运动平台上开展了跟随误差实验验证,实验结果表明,所提出方法可明显降低运动跟随误差,相比于一般的内模控制方法,控制方法可将梯形曲线定位过程平均超调量误差降低86.667%,将正弦曲线平均跟随误差降低85.950%,有效验证了该方法在精密运动平台上的自适应控制性能。Proportional-integral-derivative(PID)controllers are widely used in the field of precision control,but there are still some difficulties in the adaptive and precise adjustment of its parameters.This article is aimed at the precision motion stage driven by linear motor.By constructing internal model controller to design PID parameters,the difficulty of parameter adjustment can be reduced.In order to establish the precise mathematical model required by the internal model controller,based on the research of reinforcement learning methods,this paper proposes an adaptive internal model filter coefficient optimization method based on the probabilistic inference for learning control(PILCO)algorithm.Fitting the probabilistic dynamics model based on input filter coefficient and output error,and the filter coefficient is further optimized through strategy evaluation and strategy optimization.The proposed internal model control method is verified by following error experiments on the linear motor motion stage.The experimental results show that the method proposed in this paper can significantly reduce the motion following error.Compared with the general internal model control method,the control method in this paper can reduce the average overshoot error of the trapezoidal curve positioning process by 86.667%,and the average following error of the sinusoidal curve is reduced by 85.950%,which effectively verifies the adaptive control performance of the method in this paper on the precision motion stage.

关 键 词：内模控制器 直线电机 滤波系数 策略优化 

分 类 号：TH16[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]