基于改进粒子群算法的并联机械手运动学参数辨识

Kinematic Parameter Identification of Parallel Manipulator Based on Improved Particle Swarm Algorithm

针对Delta并联机械手在运动过程中由于受静态误差的影响,使其运动学精度降低的问题,提出了一种运动学参数辨识的新方法.首先建立含有9项主要误差源的误差模型,提出一种基于粒子分类和按维动态改变权重(PC-DCWD)的改进粒子群算法.该算法引入平均极值将粒子分类,针对不同粒子采用异步进化策略,增强种群间的协作;针对进化过程中同一粒子的不同维度所出现的维差异问题,通过引入距离因子概念,实现粒子按维动态改变惯性权重的策略.通过对五个经典函数进行测试,并与线性递减粒子群优化算法进行比较,仿真结果表明,PCDCWD算法能有效避免陷入局部最优,提高算法的精度和收敛速度,具有更强的优化性能.最后,对误差模型的9项误差源进行参数辨识,仿真结果表明,辨识值与真实值几乎相等,从而验证了所提算法的有效性、可行性.

The kinematic precision of the Delta parallel manipulator is reduced during movement because of static errors. In this study, a new method for identifying the kinematic parameters is proposed. Firstly, an error model is established that involves nine main sources of errors. A modified particle swarm optimization algorithm based on particle classification and dynamically changing weight by dimensions （PC-DCWD） is then produced. This algorithm introduces an average extreme value to classify populations, and it adopts asynchronous evolutionary strategies for different particles while enhancing collaboration between populations. This introduces the concept of a distance factor to dynamically change the inertia weight by dimensions, while considering that different dimensions of the same particle have differences when searching the best location. By comparing the PC-DCWD algorithm with the linearly decreasing weight particle swarm optimization algorithm, and testing five classical functions, the simulation result shows that the PC-DCWD falls into the local optimum effectively, increases the accuracy and convergence rate of the algorithm, and has a better performance in optimization. Finally, by identifying nine main errors in the error model, the simulation result shows that the identification value is almost equal to the ideal value. Therefore, the proposed algorithm is considered effective and practical.