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Recurrent Fuzzy Wavelet Neural Network Variable Impedance Control of Robotic Manipulators with Fuzzy Gain Dynamic Surface in an Unknown Varied Environment
Fuzzy Sets and Systems ( IF 3.9 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.fss.2020.05.001
Mohammad Hossein Hamedani , Maryam Zekri , Farid Sheikholeslam , Mario Selvaggio , Fanny Ficuciello , Bruno Siciliano

Abstract In this paper, an intelligent variable impedance control combined with a fuzzy gain dynamic surface is proposed to improve the interaction of the robot manipulator with an unknown varied environment. The parameters of the proposed variable impedance are adapted by optimization an introduced cost function using a recurrent fuzzy wavelet network. The stability conditions for the varying inertial, stiffness and damping are presented to guarantee the stability of the variable impedance. Additionally, a fuzzy dynamic surface method is developed to tune the gains of the dynamic surface as a robust controller. The proposed fuzzy gain dynamic surface is used to force the end-effector of the manipulator to track the desired impedance profile in the presence of large disturbances. Using Lyapunov's method, the stability of the mentioned closed-loop system is proved. Finally, by using a designed simulator for IRB120 (ABB) robot, several simulations are carried out to verify the performance of the proposed method for the execution of various tasks in an unknown varied environment in the presence of large disturbances.

中文翻译:

未知变化环境下具有模糊增益动态表面的机器人机械手的递归模糊小波神经网络变阻抗控制

摘要 在本文中,提出了一种结合模糊增益动态表面的智能可变阻抗控制来改善机器人机械手与未知变化环境的交互。通过使用循环模糊小波网络优化引入的成本函数来调整所提出的可变阻抗的参数。给出了变化的惯性、刚度和阻尼的稳定性条件,以保证可变阻抗的稳定性。此外,还开发了一种模糊动态表面方法来调整动态表面的增益作为鲁棒控制器。提出的模糊增益动态表面用于强制机械手的末端执行器在存在大干扰的情况下跟踪所需的阻抗曲线。使用李雅普诺夫方法,证明了上述闭环系统的稳定性。最后,通过使用为 IRB120 (ABB) 机器人设计的模拟器,进行了多次模拟,以验证所提出的方法在存在大扰动的未知变化环境中执行各种任务的性能。
更新日期:2020-05-01
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