IEEE Access (Jan 2022)

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

DOI
https://doi.org/10.1109/ACCESS.2022.3215980
Journal volume & issue
Vol. 10
pp. 123410 – 123422

Abstract

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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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