Upper limb rehabilitation robot trajectory optimization based on variable mutation hybrid particle swarm optimizationrch
Regarding the cumbersome motion trajectories of upper limb rehabilitation robots,which do not align with the daily habits of human arms,we propose the use of an improved particle swarm algorithm for time-optimal trajectory optimization.Addressing the issue of premature convergence and poor optimization stability inherent in the particle swarm algorithm,we incorporate the concept of flock foraging coordination in birds into the algorithm.A novel velocity updating formula is designed,taking into account the influences of individual extremum,group extremum,and social extremum on particle updates.This design enhances the algorithm's global optimization capability and optimization robustness in high-dimensional search spaces and under high-dimensional constraint conditions.Additionally,a stagnation mutation compensation mechanism is introduced during particle iteration to increase particle diversity,further improving the algorithm's optimization efficiency.Through MATLAB simulation experiments,the improved particle swarm algorithm reduces the average optimal time by 44.29％and decreases the average convergence iterations by 16.75％.Furthermore,the joint movements of the upper limb rehabilitation robot conform to the requirements of rehabilitation training,thereby enhancing the quality of rehabilitation training.
万方数据
维普
