基于遗传-鲸鱼优化的SCARA自适应迭代学习控制

Adaptive iterative learning control of SCARA manipulator based on genetic-whale optimization

针对SCARA重复运动后的轨迹会出现偏差的问题,给出了一种基于改进鲸鱼算法的自适应迭代学习优化控制策略。根据SCARA驱动方程,设计了动力学系统的迭代学习控制律。由于鲸鱼算法收敛速度慢,利用遗传算法与鲸鱼算法结合,提高算法的全局搜索能力。对机械臂控制器参数KP、KD进行寻优。实验结果表明,该算法灵活性好,对系统期望轨迹具有较高的跟踪精度,有效降低了双关节机械臂的位置、速度跟踪误差,具有较强的可行性与有效性。

Aiming at the problem of position and speed control when the manipulator is repeatedly moving,an adaptive iterative learning control strategy based on Genetic-Whale optimization algoritm is proposed.According to the SCARA(Selective Compliance Assembly Robot Arm)manipulator drive equation,the iterative learning control law of the dynamic system is designed.Because the whale algorithm converges slowly,the genetic algorithm is combined with the whale algorithm to improve the global search ability of the algorithm,and the parameters of the arm controller KP and KD are optimized.The experimental results show that the adaptive control systems has good flexibility,high tracking accuracy for the system’s desired trajectory,and effectively reduces the position and velocity tracking error of the double joint manipulator,which has strong feasibility and effectiveness.