Abstract: A trajectory correction method was proposed based on AHPSO algorithm for a new 3T1R parallel manipulator 2-RPaRSS, which possessed the clearances among moving pairs causing positioning deviations and the misfit between real trajectory and theoretical trajectory. The clearance errors of kinematic pairs were established, and the clearance errors of the kinematic pairs was equivalent to the rod length errors. According to the inverse kinematics equation, the position and attitude error models of 2-RPaRSS were established, the differential relation between input and output was obtained, and the input angle compensation of driving rod was introduced. The particle swarm optimization (PSO) algorithm was used to optimize the compensation, and the gap error compensation problem was transformed into the minimum fitness problem. Finally, the PSO algorithm was improved by means of hybrid weight adaptive adjustment, learning factor adaptive adjustment and chaotic disturbance adaptive adjustment strategy, and AHPSO was obtained. The simulation results show that the performance of the AHPSO algorithm has better convergence and the stability is excellent. It is an effective method to compensate the clearance errors of the motion pairs of the parallel manipulators, and the positioning accuracy is obviously improved after compensation.

Key words: parallel manipulator, kinematic pair clearance, adaptive hybrid particle swarm optimization (AHPSO) algorithm, error compensation

摘要： 针对新型3T1R并联操作手2-RPaRSS存在运动副间隙引起的定位偏差，造成操作手的实际轨迹与理论轨迹不吻合的问题，提出了一种基于自适应混合粒子群优化(AHPSO）算法的轨迹修正方法。建立操作手包含的各类运动副误差模型，在模型中将间隙误差完全等效成杆长误差；根据逆运动学方程建立并联操作手2-RPaRSS的位姿误差模型，得到关于输入、输出的微分关系式，并引入驱动杆输入角补偿量；利用粒子群优化(PSO)算法对补偿量寻优，将间隙误差补偿问题转化为求适应度极小值问题；通过混合权值自适应调整、学习因子自适应调节、混沌扰动范围自适应调节策略改进了PSO算法，得到AHPSO算法。仿真结果表明AHPSO算法性能优良，具有更好的收敛性和稳定性，对并联操作手运动副间隙误差的补偿是一种有效方法，补偿后定位精度得到了明显改善。

关键词: 并联操作手, 运动副间隙, 自适应混合粒子群优化算法, 误差补偿