Tan Ning, Yu Peng
IEEE Trans Neural Netw Learn Syst. 2024 Dec;35(12):18026-18038. doi: 10.1109/TNNLS.2023.3310744. Epub 2024 Dec 2.
This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipulators in a preset time, in spite of the initial state of manipulators. This feature will facilitate the scheduling of a series of tasks in industrial applications. To this end, a varying-parameter predefined-time convergent zeroing neural dynamics (ZND) model is first proposed and employed to solve the regulation problem. As well as the primary task, a conventional ZND model is utilized to achieve the avoidance of obstacle. The stability of the proposed controller is analyzed based on the Lyapunov stability theory. For the sake of dealing with the unknown kinematic model of robotic manipulators, gradient neural dynamics (GND) models are exploited to adapt the Jacobian matrices just relying on the control signal and sensory output, which enables us to control robotic manipulators in a model-free manner. Finally, the efficacy and merits of the proposed control method are verified by simulations and experiments, including a comparison with the existing method.
本文针对模型未知的机器人机械手，提出了一种具有预定义时间收敛性的无模型运动控制方法。预定义时间收敛特性保证了机器人机械手无论初始状态如何，都能在预设时间内完成调节任务。这一特性将有助于工业应用中一系列任务的调度。为此，首先提出并采用了一种变参数预定义时间收敛归零神经动力学（ZND）模型来解决调节问题。除了主要任务外，还利用传统的ZND模型来实现避障。基于李雅普诺夫稳定性理论分析了所提出控制器的稳定性。为了处理机器人机械手未知的运动学模型，利用梯度神经动力学（GND）模型仅依靠控制信号和传感输出对雅可比矩阵进行自适应，从而使我们能够以无模型的方式控制机器人机械手。最后，通过仿真和实验验证了所提出控制方法的有效性和优点，包括与现有方法的比较。
