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首页> 外文期刊>Neurocomputing >MLP neural network-based recursive sliding mode dynamic surface control for trajectory tracking of fully actuated surface vessel subject to unknown dynamics and input saturation
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MLP neural network-based recursive sliding mode dynamic surface control for trajectory tracking of fully actuated surface vessel subject to unknown dynamics and input saturation

机译：基于MLP神经网络的递归滑模动态表面控制，用于在未知动力学和输入饱和度的情况下对全驱动水面船只进行轨迹跟踪

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In this work, we present a MLP neural network-based recursive sliding mode dynamic surface control scheme for a fully actuated surface vessel with uncertain dynamics and external disturbances, where the control input are required to be constrained. First of all, the minimum learning parameter (MLP) neural networks (NN)-based are designed to enhance the robustness against model uncertainties. Subsequently, an adaptive law is employed to compensate neural networks approximation errors and disturbances. The recursive sliding mode control method combined with dynamic surface control (DSC) is designed to eliminate repeated derivative of virtual control laws and enhance systems robustness. A smooth hyperbolic tangent function is incorporated with the control scheme to reduce the risk of actuator saturation. At the same time, the Nussbaum function is used to compensate for the saturation function and ensure the stability of system. We show that under the proposed control method, despite the presence of system uncertainties and disturbances, the tracking errors can converge into arbitrarily small neighborhoods around zero, while the constraint requirements on the control force and torque will not be violated. By using the Lyapunov function, it is proven that the proposed control method can guarantee the uniform boundedness of all the closed loop signals. Finally, simulation results further demonstrate the effectiveness of the proposed method. (c) 2019 Elsevier B.V. All rights reserved.
机译：在这项工作中，我们提出了一种基于MLP神经网络的递归滑模动态表面控制方案，该方案用于具有不确定动力学和外部干扰的全致动水面船舶，其中需要限制控制输入。首先，基于最小学习参数（MLP）神经网络（NN）的设计旨在增强针对模型不确定性的鲁棒性。随后，采用自适应定律来补偿神经网络的近似误差和干扰。递归滑模控制方法与动态表面控制（DSC）相结合，旨在消除虚拟控制定律的反复导数并增强系统的鲁棒性。平滑的双曲正切函数与控制方案结合在一起，可降低执行器饱和的风险。同时，Nussbaum函数用于补偿饱和函数并确保系统的稳定性。我们表明，在提出的控制方法下，尽管存在系统不确定性和干扰，但跟踪误差仍可以收敛到零附近的任意小邻域，而不会违反对控制力和转矩的约束要求。通过使用李雅普诺夫函数，证明了所提出的控制方法可以保证所有闭环信号的均匀有界性。最后，仿真结果进一步证明了该方法的有效性。 （c）2019 Elsevier B.V.保留所有权利。

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