Anh H.P.H.
Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Viet Nam
Abstract:
This paper presents a detailed study to investigate the possibility of applying the online tuning gain scheduling MIMO neural dynamic DNN-PID control architecture to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm so as to improve its joint angle position output performance. The proposed controller was implemented as a subsystem to control the real-time 2-axes PAM robot-arm system so as to control precisely the joint angle position of the 2-axes PAM robot arm when subjected to system internal interactions and load variations. The results of the experiment have demonstrated the feasibility and benefits of the novel proposed control approach in comparison with the traditional PID control strategy. The proposed gain scheduling neural MIMO DNN-PID control scheme forced both joint angle outputs of 2-axes PAM robot arm to track those of the reference simultaneously under changes of the load and system coupled internal interactions. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID. These results can be applied to control other highly nonlinear systems. © 2010 Elsevier Ltd. All rights reserved.
Author Keywords:
Fast online tuning back propagation (BP) algorithm; Highly nonlinear PAM robot arm; Pneumatic artificial muscle (PAM); Proposed online tuning gain scheduling MIMO dynamic neural PID controller (MIMO DNN-PID); Real-time joint angle position control
Source title:
Expert Systems with Applications
Link:
Scorpus Link
Authors with affiliations:
-
Anh, H.P.H., Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Viet Nam
Correspondence Address:
Anh, H.P.H.; Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology, Ho Chiemail: hphanh@hcmut.edu.vn
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HCM_U121.pdf