Simple neural network compact form model-free adaptive controller for thin McKibben muscle system

• Main Authors: Abdul Hafidz, Muhamad Hazwan, Mohd Faudzi, Ahmad Athif, Norsahperi, Nor Mohd Haziq, Jamaludin, Mohd Najeb, Awang Hamid, Dayang Tiawa, Mohamaddan, Shahrol
• Format: Article
• Published: Institute of Electrical and Electronics Engineers 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/103196/; https://ieeexplore.ieee.org/document/9934849/
• Institution: Universiti Putra Malaysia

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.