引用本文:冯浩,姜金叶,宋倩玉,殷晨波,马伟,曹东辉.电液伺服系统多PID控制器参数整定优化[J].控制理论与应用,2024,41(4):763~767.[点击复制]
FENG Hao,JIANG Jin-ye,SONG Qian-yu,YIN Chen-bo,MA Wei,CAO Dong-hui.Multi-PID controller parameters optimization of electro hydraulic servo system[J].Control Theory and Technology,2024,41(4):763~767.[点击复制]
电液伺服系统多PID控制器参数整定优化
Multi-PID controller parameters optimization of electro hydraulic servo system
摘要点击 3947  全文点击 179  投稿时间：2022-04-14  修订日期：2024-01-10
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DOI编号  10.7641/CTA.2023.20271
  2024,41(4):763-767
中文关键词  电液伺服系统  机器人  轨迹控制  粒子群算法  智能控制
英文关键词  electro hydraulic servo system  robot  trajectory control  PSO  intelligent control
基金项目  国家自然科学基金项目(52105064), 江苏省自然科学基金项目(BK20221342), 国家重点研发计划项目(2021YFB2011904), 南京信息工程大学科研 启动经费项目(2021R042)
作者单位E-mail
冯浩* 南京信息工程大学 fenghao@nuist.edu.cn 
姜金叶 南京信息工程大学 计算机学院  
宋倩玉 南京信息工程大学 人工智能学院  
殷晨波 南京工业大学 挖掘机关键技术联合研究所  
马伟 南京工业大学 挖掘机关键技术联合研究所  
曹东辉 三一重机有限公司  
中文摘要
      为了解决挖掘机器人动臂、斗杆和铲斗不同电液伺服系统中多个比例–积分–微分(PID)控制器参数优化的难题, 提高挖掘机器人铲斗齿尖轨迹跟踪精度, 采用改进的粒子群算法(PSO)对多PID控制器参数进行整定优化. 首先, 建立电液伺服系统的数学机理模型, 在理论模型的基础上, 采用递推最小二乘辨识法(RLS)得到实际的机理模型. 其次, 提出一种改进的PSO算法, 采用非线性自适应惯性权重、引入异步变化策略、设计精英变异方法. 接着, 搭建仿真验证平台, 跟踪正弦轨迹, 比较传统Z-N参数整定方法、基本PSO算法和改进PSO算法的差别. 最后, 以挖掘机 器人最常见的整平为代表工况, 基于23 t挖掘机器人实验平台进行实验验证. 实验结果表明, 改进PSO算法的跟踪精度最高, 与基本PSO算法相比, 明显提高了轨迹跟踪精度
英文摘要
      In order to solve the problem of parameters optimization of multi-proportional-integral-derivative (PID) controllers in different electro-hydraulic servo systems of boom, stick and bucket for robotic excavator, and improve the tracking accuracy of bucket tooth tip, the parameters of PID controllers are optimized by an improved particle swarm optimization algorithm (PSO). Firstly, the mathematical mechanism model of electro-hydraulic servo system is established. Based on the theoretical model, the transfer function is obtained by recursive least square identification method (RLS). Secondly, an improved PSO algorithm is proposed, which adopts nonlinear adaptive inertia weight, introduces asynchronous change strategy, designs elite mutation method. Then, a simulation verification platform is build to track the step and sinusoidal trajectory, and compare the differences between the traditional Z-N method, the basic PSO algorithm and the improved PSO algorithm. Finally, taking the leveling and slope repair as the representative working condition, the experimental verification is carried out based on a 23 ton robotic excavator experimental platform. The experimental results show that the improved PSO algorithm has the highest tracking accuracy, and significantly improves the trajectory tracking accuracy compared with the basic PSO algorithm.