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Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator

机译:ch机器人机械手的混合神经网络分数积分终端滑模控制

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This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm robot manipulator. A fraction integral terminal sliding mode control applies to the Inchworm robot manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.
机译:提出了一种基于分数积分终端滑模控制和自适应神经网络的控制方案。它处理系统模型的不确定性和干扰,以提高Inchworm机械手的控制性能。分数积分终端滑模控制应用于Inchworm机械手以获得初始稳定性。此外,还设计了一种自适应神经网络来近似系统不确定性和未知干扰,以减少抖动现象。所提出的自适应神经网络的权重矩阵可以根据当前状态误差信息在线更新。 Lyapunov理论证明了所提出控制方法的稳定性。将自适应神经网络分数积分终端滑模控制的性能与其他三个常规控制器进行了比较,例如滑模控制,积分终端滑模控制和分数积分终端滑模控制。仿真结果表明了该控制方法的有效性。

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