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Exponentially convergence for the regressor-free adaptive fuzzy impedance control of robots by gradient descent algorithm
International Journal of Systems Science ( IF 4.3 ) Pub Date : 2020-07-07 , DOI: 10.1080/00207721.2020.1780513
Gholamreza Nazmara 1 , Mohammad Mehdi Fateh 1 , Seyed Mohammad Ahmadi 1
Affiliation  

Having the capability to estimate parametric and non-parametric uncertainties, this paper investigates a regressor-free adaptive model-reference scheme for impedance control of robotic systems interacting with an environment. Using the gradient descent algorithm for designing a fuzzy estimator makes the tune of controller’s parameters possible in different physical situations and uncertainties. Thanks to the voltage control strategy and fuzzy systems, there is no need for designers to have the knowledge of robots and actuators’ dynamics. In addition, the force feedback is not employed in the structure of control law. Using the Lyapunov-like stability analysis, not only the exponential convergence of error signal and its time derivative to zero are guaranteed in the decentralised form but also the boundedness of all signals is ensured. To confirm the efficiency of proposed control algorithm, several simulations as well as a comparison with a positioned-based impedance controller and a regressor-based adaptive impedance controller are conducted on a two-link planar robot manipulator considering a tunable desired trajectory and different stiffness of environment. Additionally, the proposed adaptive impedance controller is applied to a six degree of freedom revolute-joint manipulator to provide better evaluation.

中文翻译:

基于梯度下降算法的机器人无回归自适应模糊阻抗控制的指数收敛

本文具有估计参数和非参数不确定性的能力,研究了一种无回归量的自适应模型参考方案,用于与环境交互的机器人系统的阻抗控制。使用梯度下降算法设计模糊估计器可以在不同的物理情况和不确定性下调整控制器的参数。由于采用了电压控制策略和模糊系统,设计人员无需了解机器人和执行器的动力学知识。此外,控制律结构中没有采用力反馈。利用类李雅普诺夫稳定性分析,不仅保证了误差信号的指数收敛及其对零的时间导数在分散形式下,而且保证了所有信号的有界性。为了确认所提出的控制算法的效率,在考虑可调期望轨迹和不同刚度的双连杆平面机器人机械手上进行了几次模拟以及与基于定位的阻抗控制器和基于回归量的自适应阻抗控制器的比较环境。此外,所提出的自适应阻抗控制器应用于六自由度旋转关节机械手,以提供更好的评​​估。
更新日期:2020-07-07
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