电液伺服系统多PID控制器参数整定优化

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

Multi-PID controller parameters optimization of electro hydraulic servo system

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 op-timization 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). Sec-ondly, 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 exper-imental 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.