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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)123410-123422
Number of pages13
JournalIEEE Access
Publication statusPublished - 2022


  • Artificial neural networks
  • control and learning for soft robots
  • hydraulic/pneumatic actuators
  • model-free adaptive controller
  • modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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