A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments. (1st January 2021)

• Record Type: Journal Article
• Authors: Chen, Yiheng; Sun, Ning; Liang, Dingkun; Qin, Yanding; Fang, Yongchun
• Is Part Of: Mechanical systems and signal processing. Volume 146(2021)
• Publication Date: 2021-01-01
• Subjects: Pneumatic artificial muscle (PAM) -- Neural network (NN) -- Neuroadaptive control; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106976
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The paper realizes neuroadaptive control for PAMs with complicated nonlinear dynamics. The tracking errors converge to zero asymptotically and never exceed preset bounds. The closed-loop asymptotic stability is rigorously proven. Hardware experiments verify the effectiveness of the proposed method. Abstract: 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.