A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments
A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments
复制标题
一种气动人工肌肉系统神经自适应控制方法的硬件实验
DOI：
10.1016/j.ymssp.2020.106976
复制
发表时间：
2021-01-01
影响因子：
8.4
通讯作者：
Fang, Yongchun
中科院分区：
文献类型：
--
作者：
Chen, Yiheng;Sun, Ning;Fang, Yongchun
气动人工肌肉(PAM)执行器是一种仿生执行器，在仿生机器人和医疗辅助设备中有着广泛的应用。然而，PAM系统通常具有高度的非线性、不确定性和时变特性，这给精确的动态建模和控制器设计带来了挑战。针对上述问题，本文提出了一种神经自适应控制方法来处理系统的不确定性，并获得满意的跟踪性能。首先，为了在线补偿PAM系统动态模型中包含的未知非线性项，采用了三层神经网络。其次，利用滤波后的信号，可以有效地解决代数环路问题。然后，基于滑模面设计了一种非线性鲁棒控制器。该方法保证了PAM系统跟踪误差的渐近收敛，并且在控制过程中始终将跟踪误差控制在预定的范围内。此外，利用Lyapunov方法从理论上证明了闭环系统的稳定性。最后，在自制的PAM实验台上进行了一系列硬件实验，验证了所提出的神经自适应控制方法的有效性和鲁棒性。(C)2020爱思唯尔有限公司。保留所有权利。
Pneumatic artificial muscle (PAM) actuators are a kind of biomimetic actuators, which are being widely used in the applications of biomimetic robots and medical auxiliary devices. However, PAM systems usually have high nonlinearities, uncertainties, and time-varying characteristics, which bring challenges for accurate dynamic modeling and controller design. To deal with the above issues, in this paper, a neuroadaptive control method is proposed to handle the system uncertainties and achieve satisfactory tracking performance. First, in order to compensate the unknown nonlinear term involved in the dynamic model of the PAM system online, a three-layer neural network is utilized. Next, by means of the filtered signal, the algebraic loop problem can be solved effectively. Then, based on a sliding mode surface, a nonlinear robust controller is designed. By using the proposed method, the asymptotic convergence of tracking errors of the PAM system is guaranteed, and the tracking errors are always restricted within preset bounds during the control process. Moreover, the stability of the closed-loop system is proven theoretically by utilizing Lyapunov techniques. Finally, a series of hardware experiments are implemented on a self-built PAM testbed to validate the effectiveness and robustness of the proposed neuroadaptive control method. (C) 2020 Elsevier Ltd. All rights reserved.