Paper Info
Title
Iterative Feedback Tuning-Based Model-Free Adaptive Iterative Learning Control Of Pneumatic Artificial Muscle
Abstract
Iterative feedback tuning (IFT) method is a data-driven control method, which can tune the parameters of the system controller without knowing the system model. Pneumatic artificial muscles (PAMs) are flexible actuators that are widely used in the field of rehabilitation robots because of their flexibility and light weight. However, its nonlinearity, difficult modeling and time-varying parameters make it difficult to control. In this paper, a model-free adaptive iterative learning control (MFAILC) method based on IFT is proposed for a strong nonlinear system such as PAM. The method obtains the dynamic linearization model of PAM behavior according to the dynamic linearization theorem, then designs the controller structure, and finally uses the IFT method to optimize the controller parameters. The method proposed in this paper was compared with the MFAILC method. The simulation results show that the proposed method has a faster convergence speed and smaller tracking errors in the desired trajectory tracking control, and its effectiveness is also verified.
Year
DOI
Venue
2019
10.1109/AIM.2019.8868584
2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
Field
DocType
ISSN
Control theory,Control theory,Computer science,Adaptive system,Control engineering,Pneumatic artificial muscles,Iterative learning control,Artificial muscle,Linearization,System model,Hartman–Grobman theorem
Conference
2159-6255
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Weifeng Wu100.34
Da Li200.34
Wei Meng329430.14
Jie Zuo411115.62
Quan Liu52812.17
Qingsong Ai64315.50