【作者】 谢宏； 袁小芳； 向启均； 陈祎婧； 王立宸；

【Author】 Hong Xie;Xiaofang Yuan;Qijun Xiang;Yijing Chen;Lichen WANG;College of Electrical and Information Engineering, Hunan University;

【机构】 湖南大学电气与信息工程学院；

【摘要】 针对标准遗传算法的局限和不足,将模拟退火算法与遗传算法结合,改进模拟退火优化遗传算法通过小区间生成、交叉变异参数自适应、启发式交叉算子和精英保留策略等对其进行了改进和优化。为了获得更准确的机器人动力学参数和动力学模型,通过名义动力学参数估计机器人运动过程中各关节所受力的大小与真实所受力大小的差值建立误差模型,并采用改进的遗传算法对机器人动力学参数进行辨识。对工业六自由度机器人仿真结果表明,改进后算法相比于常规遗传算法和自适应参数遗传算法在参数辨识上的辨识精度和收敛速度上均有提高。更多还原

【Abstract】 In view of the advantages and disadvantages of the standard genetic algorithm, the paper combines the simulated annealing algorithm with genetic algorithm. The improved simulated annealing and optimized genetic algorithm has been improved by using inter cell generation, crossover and mutation parameter adaptation, heuristic crossover operator and elitist retention strategy. In order to obtain the more accurate dynamic parameters and the more accurate dynamic model, the error model is established by using the name dynamic parameters to estimate the difference between the forces of robot joints in the moving process, and the above improved optimized genetic algorithm is adopted to estimate the robot dynamic parameters. The simulation results of an industrial six degree of freedom robot show that the convergence speed and accuracy of the improved algorithm are improved by comparing with the conventional genetic algorithm and adaptive parameters genetic algorithm.更多还原