文章摘要
马浩兴,王东红,罗文龙.基于多种群遗传算法的液压系统PID参数寻优[J].包装工程,2020,41(23):204-210.
MA Hao-xing,WANG Dong-hong,LUO Wen-long.PID Parameter Optimization of Hydraulic System Based on Multi-population Genetic Algorithm[J].Packaging Engineering,2020,41(23):204-210.
基于多种群遗传算法的液压系统PID参数寻优
PID Parameter Optimization of Hydraulic System Based on Multi-population Genetic Algorithm
投稿时间:2020-02-24  
DOI:10.19554/j.cnki.1001-3563.2020.23.028
中文关键词: 液压顶升系统  多种群遗传算法  参数整定  参数优化
英文关键词: hydraulic jacking system  multi-population genetic algorithm  parameter setting  parameter optimization
基金项目:
作者单位
马浩兴 西安建筑科技大学西安 710055 
王东红 西安建筑科技大学西安 710055
中国建筑科学研究院有限公司建筑机械化研究分院河北 廊坊 065000 
罗文龙 西安建筑科技大学西安 710055
中国建筑科学研究院有限公司建筑机械化研究分院河北 廊坊 065000 
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中文摘要:
      目的 针对传统PID在对包装运输液压顶升系统控制中参数难以整定的问题,使用多种群遗传算法(MPGA)对参数寻优。方法 采用多种群遗传算法,将算法与常规PID控制相结合。对液压系统进行分析,建立起液压系统的数学模型,将其运用到算法优化后PID控制策略的被控对象中。同时,与一般遗传算法优化后的参数进行仿真对比,考察多种群遗传算法对PID控制策略优化的有效性。结果 仿真结果表明,多种群遗传算法优化后的参数能使被控对象很快地收敛于稳态。整个系统响应速度快、稳态误差小、超调量小,而一般的遗传算法得到的参数陷入局部最优,无法在较短时间内得到全局最优解。结论 所提出的优化算法对PID参数整定有良好的效果,能满足系统的控制要求。
英文摘要:
      The work aims to use multi-population genetic algorithm (MPGA) to optimize the parameters to solve the difficulty in setting parameters in the control of the traditional PID hydraulic jacking system for packaging and transportation. The multi-population genetic algorithm was used to combine the algorithm with conventional PID control. The hydraulic system was analyzed. A mathematical model of the hydraulic system was established and applied to the controlled object of the PID control strategy after the algorithm optimization. The effectiveness of multi-population genetic algorithm for PID control strategy optimization was investigated by comparing with the optimized parameters of general genetic algorithm. The simulation result showed that, parameters optimized by the multi-population genetic algorithm can make the controlled object converge to steady state quickly. The whole system had fast response speed, small steady-state error and small overshoot. However, the parameters obtained by general genetic algorithm fell into local optimum and cannot get global optimum solution in a short time. The proposed optimization algorithm has a good effect on PID parameter setting and can meet the control requirements of the system.
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