On-Board Evolution of a Model-Free Adaptive Controller for a Robotic Fish

Introduction Many physical systems experience fluctuating dynamics throughout their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. Designing control strategies that mitigate the negative effects of such variations can be difficult. One approach is to utilize model-free adaptive control (MFAC), which learns how to control a system by continually updating link weights of an artificial neural network (ANN) (Cheng, 2004). However, determining the optimal values of various control parameters, as well as the structure of the ANN, is challenging. In this study, we investigate how to enhance the on-board adaptability of MFAC-based systems through computational evolution.