Attitude PID control parameter tuning of curtain wall cleaning robot based on improved genetic algorithm
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摘要: 针对高楼幕墙清洗机器人姿态PID参数整定耗时长，误差大的问题，提出一种改进遗传算法(improved genetic algorithm, IGA)。引入halton序列作为初始种群，在交叉和变异环节引入自适应动态调节机制，提出综合性能更好的改进时间加权绝对误差积分(improved integral of time-weighted absolute error, IITAE)评价指标函数。使用IGA以及人工经验法、AGPSO算法、GA算法等进行PID参数整定试验，结果表明，IGA算法的整定结果在目标函数值上较其他算法提高20%以上；在收敛时间上降低50%以上。IGA方法设计的控制器能够实现机器人姿态的稳定控制，在幕墙清洗机器人空中姿态的稳定控制方面，具有较好的应用价值。Abstract: To address the problem of time-consuming and large errors in the attitude PID parameter tuning of a high-rise curtain wall cleaning robot, an improved genetic algorithm (IGA) was proposed. The halton sequence was introduced as the initial population, the adaptive dynamic regulation mechanism was introduced in the crossover and variation stages, and improved integral of time-weighted absolute error (IITAE) evaluation index function with better comprehensive performance was proposed. PID parameter tuning experiments were carried out using IGA, manual empirical method, AGPSO and GA. The results show that the tuning results of the IGA algorithm are more than 20% higher than other algorithms in terms of the objective function value and reduced the convergence time by more than 50%. The controller designed by the IGA methodcan achieve stable control of robot attitude, which has good application value for air attitude stability control of curtain wall cleaning robot.
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Key words:
- curtain wall cleaning robot /
- attitude control /
- PID tuning /
- genetic algorithm (GA)
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图 2 幕墙清洗机器人越障原理图
Figure 2. Schematic diagram of barrier crossing for curtain wall cleaning robot
图 4 遗传算法优化PID控制系统参数的原理图
Figure 4. Schematic diagram of optimizing PID control system parameters by genetic algorithm
图 5 幕墙清洗机器人位置姿态控制系统仿真框图
Figure 5. Curtain wall cleaning robot position attitude control system simulation block diagram
图 7 4种方法对应的系统开环Bode图
Figure 7. Corresponding system open-loop Bode diagram of four methods
表 1 算法主要参数
Table 1. Main parameters of algorithm
算法 参数 取值 AGPSO 惯性系数最小值ωmin 0.6 惯性系数最大值ωmax 0.9 学习因子C1初始值C1begin 2.2 学习因素C1最终值C1end 0.8 学习因子C2初始值C2begin 0.8 学习因素C2最终值C2end 2.2 GA 交叉概率Pc 0.6 变异概率Pm 0.08 IGA 交叉概率最小值Pcmin 0.5 交叉概率最大值Pcmax 0.9 变异概率最小值Pmmin 0.005 变异概率最大值Pmmax 0.09 
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表 2 4种方法整定结果
Table 2. Tuning results of four methods
参数 经验法 AGPSO GA IGA Kp1 40 71.7452 52.4645 11.5132 Ki1 2 0.5627 5.7563 2.5258 Kd1 0.01 3.2768 1.4225 0.7854 Kp2 23.5 12.7365 34.5148 23.4314 Ki2 1.23 0.8685 15.4132 1.3391 Kd2 2.1 12.8285 0.2154 5.1437
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