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Neural Network-Based Asymptotic Tracking Control of Unknown Nonlinear Systems with Continuous Control Command
International Journal of Control ( IF 2.1 ) Pub Date : 2018-07-13 , DOI: 10.1080/00207179.2018.1494388
Hamed Jabbari Asl 1, 2 , Mahdieh Babaiasl 3 , Tatsuo Narikiyo 1
Affiliation  

ABSTRACT This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.

中文翻译:

基于神经网络的具有连续控制命令的未知非线性系统的渐近跟踪控制

摘要 本文提出了一类具有时变不确定性的非线性二阶系统的鲁棒跟踪控制器。该控制器主要基于误差符号的稳健积分 (RISE) 控制方法,以在存在附加不确定性的情况下通过连续控制命令实现渐近稳定性结果。自适应前馈神经网络控制项与新的 RISE 控制器相结合,以改善系统的瞬态性能。建议的 RISE 控制器是现有饱和 RISE 控制器的修改版本,因此只需要输出导数的符号。闭环系统的稳定性得到了很好的研究,证明了局部渐近稳定性。
更新日期:2018-07-13
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