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

Abstract

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 specifically to enhance the CFMFAC response. The prominent control parameters of the CFMFAC are combined and one 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 demonstrate the superiority of the proposed method over the conventional CFMFAC is confirmed by a 76% improvement in the 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.