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Zhang Changsheng, Ma Ze′ nan, Li Kuan, Chen Biaofa, Li Wei, Yang Jun. Adaptive Iterative Learning Control of SCARA Manipulator with Improved Wolf Pack Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering. doi: 10.13433/j.cnki.1003-8728.20190227
Citation: Zhang Changsheng, Ma Ze′ nan, Li Kuan, Chen Biaofa, Li Wei, Yang Jun. Adaptive Iterative Learning Control of SCARA Manipulator with Improved Wolf Pack Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering. doi: 10.13433/j.cnki.1003-8728.20190227

Adaptive Iterative Learning Control of SCARA Manipulator with Improved Wolf Pack Algorithm

doi: 10.13433/j.cnki.1003-8728.20190227
  • Received Date: 2019-04-07
    Available Online: 2020-12-29
  • Aiming at reducing the position and speed tracking error of robot repetitive motion, an adaptive iterative learning control strategy based on wolf group algorithm is presented in this paper. According to the SCARA (Selective compliance assembly robot arm) manipulator drive equation, the iterative learning control law of the dynamic system is designed. The wolf group algorithm with adaptive step size is introduced to enable the wolf to dynamically adjust the moving step size according to the prey odor concentration, which improves the convergence speed and accuracy. When the strategy optimizes the parameters of the arm controller KP and KD, the result is effect for control and achieves effective tracking of the desired trajectory. 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.
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