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针对压电智能柔性机械臂的挠度形变振动问题,建立了一种基于改进的遗传算法的压电片参数优化方法。由于传统遗传算法在优化到某些代时会丢失优良遗传基因,因此发展了精英交配策略,以最大限度地保留了优良基因。本文首先建立了机械臂非线性主动控制模型,然后以平均对数衰减率为优化目标函数,采用改进遗传算法的优化策略,对压电片位置、长度、振动控制参数进行了优化。将优化结果与传统遗传算法及数值仿真实验结果进行了比较,结果表明:改进的遗传算法在第18代得到最优值,而传统遗传算法在第25代得到最优值,改进算法比传统遗传算法的收敛速度快,改进遗传算法最优化位置振动控制在1s时使臂2中点振幅降低约为数值仿真实验法的五倍;另外,当优化参数的数量增多时,改进的遗传算法仍然能够快速得到最优结果。
Aiming at the deflection deformation and vibration of piezoelectric intelligent flexible manipulator, a parameter optimization method based on improved genetic algorithm is proposed. Because traditional genetic algorithms lose good genes when optimized to certain generations, elite mating strategies have been developed to maximize the retention of good genes. In this paper, the nonlinear active control model of robotic manipulator is established firstly. Then the optimization logarithm of the logarithmic decay rate is taken as the optimization objective function. The optimization strategy of the improved genetic algorithm is used to optimize the control parameters of the position, length and vibration. The results of optimization are compared with those of traditional genetic algorithm and numerical simulation. The results show that the improved genetic algorithm obtains the optimal value in the 18th generation, while the traditional genetic algorithm obtains the optimal value in the 25th generation. The improved algorithm is better than the traditional genetic algorithm The convergence speed of the algorithm is fast, and the optimal amplitude of the genetic algorithm is about five times lower than that of the numerical simulation when the vibration control at 1s is applied. In addition, when the number of optimization parameters increases, the improved genetic algorithm can still Get the best result quickly