Xu Jiahui, Li Dazi, Zhang Jinhui
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
ISA Trans. 2023 Dec;143:630-646. doi: 10.1016/j.isatra.2023.09.020. Epub 2023 Sep 20.
With the development of industrial automation comes an ever, broadening number of application scenarios for manipulators along with increasing demands for their precise control. However, manipulator trajectory tracking control schemes often exhibit problems such as those related to high levels of coupling, complex calculations, and in various difficulties in application for industrial environments. For the problems of low accuracy in control and poor robustness of multiple-jointed robotic trajectory tracking, iterative learning control (ILC) with model compensation (MC) based on extended state observer (ESO) has been proposed for the trajectory tracking control of six-degrees-of-freedom (six-DOF) manipulators. The scheme has excellent features to overcome uncertainties in repetitive tasks, including unknown bounded perturbations that are external to the model or dynamic perturbations that are internal to the model. The proposed control strategy combines ESO, iterative learning, and MC, for precise control of trajectory tracking. Here, ESO is used to estimate disturbances, iterative learning allows fast and accurate control in repeated tasks, and the model-compensated control algorithm alleviates the necessary for many inverse operations. The convergence of our proposed control scheme is proved through Lyapunov function and time-varying approximation theory. Simulation and experimental results verify the validity of the proposed scheme.
随着工业自动化的发展，机械手的应用场景不断拓宽，对其精确控制的要求也日益提高。然而，机械手轨迹跟踪控制方案常常存在诸如耦合程度高、计算复杂以及在工业环境应用中面临各种困难等问题。针对多关节机器人轨迹跟踪控制精度低和鲁棒性差的问题，提出了基于扩展状态观测器（ESO）的带模型补偿（MC）的迭代学习控制（ILC），用于六自由度（六DOF）机械手的轨迹跟踪控制。该方案具有出色的特性，能够克服重复任务中的不确定性，包括模型外部的未知有界扰动或模型内部的动态扰动。所提出的控制策略将ESO、迭代学习和MC相结合，实现对轨迹跟踪的精确控制。在此，ESO用于估计扰动，迭代学习允许在重复任务中进行快速准确控制，模型补偿控制算法减轻了许多逆运算的必要性。通过李雅普诺夫函数和时变逼近理论证明了所提控制方案的收敛性。仿真和实验结果验证了所提方案的有效性。
