Simple Neural Network Compact Form Model-Free Adaptive Controller for Thin McKibben Muscle System

Muhamad Hazwan Abdul Hafidz, Ahmad Athif Mohd Faudzi, Nor Mohd Haziq Norsahperi, Mohd Najeb Jamaludin, Dayang Tiawa Awang Hamid, Shahrol Mohamaddan

研究成果: Article査読


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.

ジャーナルIEEE Access
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)
  • 電子工学および電気工学


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