Citation
Abstract
This paper proposes a simple neural network compact form model-free adaptive controller (NNCFMFAC) for a single thin McKibben muscle (TMM) system. The main contribution of this work is the simplification of the current neural network (NN) based compact form model-free adaptive controller (CFMFAC), which requires only two adaptive weights. This is achieved by designing a NN topology to specifically enhance the CFMFAC response. The prominent control parameters of the CFMFAC are combined and an adaptive weight is used for self-tuning, while the second adaptive weight is used to minimize the offset at each operating point. Hence the issues of redundant adaptive weights in complex neuro-based CFMFACs and slow response of the CFMFAC are significantly addressed. The idea is proven in three ways: analytically, simulation on a nonlinear system and experiments on a TMM platform. Experimental results demonstrating the superiority of the proposed method over the conventional CFMFAC is confirmed by a 76% improvement in convergence speed and a 60% reduction in root mean square error (RMSE). It is envisaged that the proposed controller can be very useful for TMM driven applications as it is model-independent, has fast response, high tracking accuracy, and minimal complexity.
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Official URL or Download Paper: https://ieeexplore.ieee.org/document/9934849/
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Engineering |
DOI Number: | https://doi.org/10.1109/access.2022.3215980 |
Publisher: | Institute of Electrical and Electronics Engineers |
Keywords: | Artificial neural networks; Control and learning for soft robots; Hydraulic/pneumatic actuators; Model-free adaptive controller; Modeling |
Depositing User: | Mr. Mohamad Syahrul Nizam Md Ishak |
Date Deposited: | 28 Jun 2024 10:02 |
Last Modified: | 28 Jun 2024 10:02 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/access.2022.3215980 |
URI: | http://psasir.upm.edu.my/id/eprint/103196 |
Statistic Details: | View Download Statistic |
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