Robotic manipulators are widely used in modern industry. In order for robot manipulators to achieve trajectory tracking control, the end-effectors must move precisely along the given trajectories. The nonlinearity, strong coupling, and uncertainty of the system make trajectory tracking very complicated and difficult. An approach for robust feedback control is presented in this paper as a solution to this problem. The design relies on active disturbance rejection control to estimate unknown disturbances affecting the plant. The controlled system is an uncertain two-link manipulator with elastic gear transmissions, described by nonlinear friction and elasticity. It is based on an industrial benchmark problem. The model has two significant uncertainties: parametric uncertainty as well as external disturbances that affect tools and motors. A simulation-based evaluation of the proposed control method and a comparison of its performance, considering specific robustness requirements and trajectory tracking, are presented. The comparison includes the baseline controller, the QFD (Quality Function Deployment) method, and PD-CNN (PD Control Compensation based on the Cascade Neural Network). Computer simulations of the proposed controller are carried out to validate the ABB (Asea Brown Boveri) industrial benchmark. The simulation results demonstrate superiority in meeting stability and target value requirements. The stability and ultimate boundedness of the tracking error of ADRC (Active Disturbance Rejection Control) are demonstrated.

中文翻译：

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柔性工业机械臂的自抗扰控制：MIMO基准问题

机器人机械手在现代工业中得到广泛应用。为了使机器人机械手实现轨迹跟踪控制，末端执行器必须沿着给定的轨迹精确运动。系统的非线性、强耦合性和不确定性使得轨迹跟踪变得非常复杂和困难。本文提出了一种鲁棒反馈控制方法作为该问题的解决方案。该设计依靠主动抗扰控制来估计影响工厂的未知扰动。受控系统是一个具有弹性齿轮传动的不确定双连杆机械手，用非线性摩擦和弹性来描述。它基于工业基准问题。该模型有两个显着的不确定性：参数不确定性以及影响工具和电机的外部干扰。考虑到特定的鲁棒性要求和轨迹跟踪，对所提出的控制方法进行了基于仿真的评估，并对其性能进行了比较。比较包括基线控制器、QFD（质量函数部署）方法和PD-CNN（基于级联神经网络的PD控制补偿）。对所提出的控制器进行计算机模拟以验证 ABB (Asea Brown Boveri) 工业基准。仿真结果表明在满足稳定性和目标值要求方面具有优越性。论证了ADRC（有源抗扰控制）跟踪误差的稳定性和极限有界性。