Optimal learning gain selection in model reference iterative learning control algorithms for human motor systems

Abstract

The role of learning gains in the ability of a computational framework to better capture the behaviour of human motor control in learning and executing a task is the subject of discussion in this paper. In our previous work, a computational model for human motor learning of a task through repetition was established and its convergence analysed. In this paper, the performance of the model is investigated through the addition of degrees of freedom in selecting learning gains, specifically the ability to independently select the learning gain for the damping term. A particle swarm optimisation (PSO) algorithm is utilised to obtain a set of gains optimised to reduce the discrepancy between the e..