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首页> 外文期刊>Neurocomputing >Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations
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Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations

机译：输入饱和下不确定Euler-Lagrange系统的强大自适应神经实用定时跟踪控制

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摘要

This paper develops a robust adaptive neural practical fixed-time tracking control scheme for Euler-Lagrange systems (ELSs) with unknown dynamics and external disturbances under input saturations. A novel auxiliary dynamic system governed by a smooth piecewise continuous function is constructed to handle the input saturation effect, while promoting to achieve the fixed-time convergence of tracking errors. Moreover, the unknown dynamics of ELSs and the bound vector of unknown external disturbances are synthesized into a compounded uncertain vector in this paper. Here, adaptive neural networks with the epsilon-modification updating laws are employed to only approximate the compounded uncertain vector, rather than each dynamic matrix of ELSs, such that the computational burden of the developed control scheme is significantly reduced. It is theoretically proven that the trajectory tracking is able to be achieved in a fixed time under the developed adaptive neural tracking control scheme, while all signals in the Euler-Lagrange closed-loop tracking control system are bounded. The simulation results on a 2-link robotic manipulator are included to illuminate the effectiveness of our developed tracking control scheme and superiority to a finite-time control scheme. (C) 2020 Elsevier B.V. All rights reserved.
机译：本文开发了具有未知动力学和输入饱和下的动态和外部干扰的欧拉拉格朗日系统（ELS）的强大自适应神经实用固定时间跟踪控制方案。由平滑分段连续功能控制的新型辅助动态系统以处理输入饱和效果，同时促进达到跟踪误差的定时收敛。此外，在本文中合成了ELS的未知动态和未知外部干扰的结合载体。这里，采用具有epsilon修改的自适应神经网络，仅采用近似复合的不确定载体，而不是els的每个动态矩阵，使得开发控制方案的计算负担显着降低。理论上证明，轨迹跟踪能够在开发的自适应神经跟踪控制方案下的固定时间内实现，而欧拉拉格朗日闭环跟踪控制系统中的所有信号都被界定。包括在2连杆机器人机械手上的仿真结果，以照亮我们开发的跟踪控制方案的有效性和对有限时间控制方案的优越性。 （c）2020 Elsevier B.v.保留所有权利。

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