期刊
IEEE ACCESS
卷 10, 期 -, 页码 123410-123422出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3215980
关键词
Adaptation models; Mathematical models; Biological neural networks; Artificial neural networks; Actuators; Hydraulic systems; Load modeling; Artificial muscles; hydraulic; pneumatic actuators; model-free adaptive controller; modeling; control and learning for soft robots
资金
- Ministry of Higher Education Malaysia (MOHE) through Fundamental Research Grant Scheme [FRGS/1/2019/TK04/UTM/02/41]
The paper introduces a simple neural network compact form model-free adaptive controller for a single TMM system, which shows significant improvements in convergence speed and error reduction.
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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