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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Adaptive neuro-predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov-based approach
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Adaptive neuro-predictive control for redundant robot manipulators in presence of static and dynamic obstacles: A Lyapunov-based approach

机译：具有静态和动态障碍物的冗余机器人机械手的自适应神经预测控制：一种基于Lyapunov的方法

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摘要

This paper presents a novel approach for online path tracking and obstacle avoidance of redundant robot manipulators. To this end, a nonlinear model predictive control (NMPC) method is designed that can track a desired path or reaches a moving target in the Cartesian space while avoiding static or moving obstacles as well as singular configurations in the workspace of the robot. The finite cost function of the NMPC is optimized at every sampling time, yielding an online optimal approach. In order to avoid collisions with moving obstacles and, at the same time, capturing a moving target, the future positions of the obstacles and the moving target are predicted using artificial neural networks (ANNs). Moreover, ANNs are employed to find a proper nonlinear model for the NMPC. The adaptation laws for the ANNs are obtained using the Lyapunov's direct method. The advantages of the proposed method are fourfold: (ⅰ) an adaptive and optimal approach is obtained, which can cope with changes in the system parameters; (ⅱ) no inverse kinematics of the redundant manipulators is required; (ⅲ) no prior knowledge about the obstacles and motion of the moving object is required; and (ⅳ) stability of the closed-loop system is guaranteed. Numerical simulations, performed on a four degree-of-freedom redundant spatial manipulator actuated by DC servomotors, show effectiveness of the proposed method.
机译：本文提出了一种用于冗余机器人操纵器的在线路径跟踪和避障的新颖方法。为此，设计了一种非线性模型预测控制（NMPC）方法，该方法可以跟踪笛卡尔空间中的所需路径或到达移动目标，同时避免机器人工作空间中的静态或移动障碍以及奇异配置。 NMPC的有限成本函数在每个采样时间都得到了优化，从而产生了在线最优方法。为了避免与移动障碍物发生碰撞并同时捕获移动目标，使用人工神经网络（ANN）预测了障碍物和移动目标的未来位置。此外，采用人工神经网络为NMPC寻找合适的非线性模型。人工神经网络的自适应律是使用李雅普诺夫直接法获得的。该方法的优点有四个方面：（:)获得了一种自适应的，最优的方法，可以应对系统参数的变化； （ⅱ）不需要冗余机械手的逆运动学； （ⅲ）无需事先了解移动物体的障碍物和运动； （ⅳ）保证了闭环系统的稳定性。在由直流伺服电动机驱动的四自由度冗余空间操纵器上进行的数值模拟表明了该方法的有效性。

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