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首页> 外文期刊>Control and Intelligent Systems >INTELLIGENT MOTION CONTROL USING FUZZY AND RECURRENT NEURAL NETWORKS FOR ROBOT MANIPULATORS IN DYNAMIC ENVIRONMENTS
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INTELLIGENT MOTION CONTROL USING FUZZY AND RECURRENT NEURAL NETWORKS FOR ROBOT MANIPULATORS IN DYNAMIC ENVIRONMENTS

机译：动态环境中基于模糊递归神经网络的机器人运动智能控制

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摘要

This article presents an Intelligent Dynamic Motion Planning (IDMP) scheme based on fuzzy technique for the idea of artificial potential fields (APF) using analytic harmonic functions. Unlike fuzzy technique, the development of APF is a computationally intensive operation. An integration of robust controller and modified Elman neural networks (MENN) approximation-based computed-torque controller is proposed to deal with unmodelled bounded disturbances and unstructured unmodelled dynamics of the robot arm. The MENN weights are tuned on-line, with no off-line learning phase required. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it can choose its way to its goal autonomously, while reacting in realtime to unexpected events. To achieve this degree of independence, the robot system needs a variety of sensors to be able to interact with the real world. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptating to changes in. manipulator dynamics.
机译：本文提出了一种基于模糊技术的智能动态运动计划（IDMP）方案，用于使用解析谐波函数的人工势场（APF）的思想。与模糊技术不同，APF的开发是计算密集型操作。提出了鲁棒控制器和改进的基于Elman神经网络（MENN）近似的计算转矩控制器的集成，以处理机械臂的无模型的有界干扰和无结构的无模型动力学。 MENN权重是在线调整的，不需要离线学习阶段。强大的神经模糊控制器的目的是为机器人的伺服系统生成命令，以便它可以自主选择实现其目标的方式，同时对意外事件做出实时反应。为了实现这种独立性，机器人系统需要各种传感器才能与现实世界互动。 Lyapunov理论保证了由非线性机器人动力学和鲁棒的神经模糊控制器组成的整个闭环系统的稳定性。提议的方案已成功测试。该控制器还展示了在适应机械手动态变化方面的卓越性能。

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