Fuzzy Neural Network Control of a Flexible Robotic Manipulator Using Assumed Mode Method

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5214 - 5227
• Main Authors: Sun, Changyin, Gao, Hejia, He, Wei, Yu, Yao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; assumed mode method (AMM); Computer simulation; Control stability; dynamic modeling; Dynamic models; Feasibility studies; flexible robotic manipulator; Flexible structures; Fuzzy control; Fuzzy logic; Fuzzy systems; Manipulator dynamics; Network control; Neural networks; neural networks (NNs); Robot arms; Strain; Vibration; vibration control
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2017.2743103

In this paper, in order to analyze the single-link flexible structure, the assumed mode method is employed to develop the dynamic model. Based on the discrete dynamic model, fuzzy neural network (NN) control is investigated to track the desired trajectory accurately and to suppress the flexible vibration maximally. To ensure the stability rigorously as the goal, the system is proved to be uniform ultimate boundedness by Lyapunov's stability method. Eventually, simulations verify that the proposed control strategy is effective, and the control performance is compared with the proportion derivative control. The experiments are implemented on the Quanser platform to further demonstrate the feasibility of the proposed fuzzy NN control.