Parameter estimation of Pendubot model using modified differential evolution algorithm

Parameter estimation plays a critical role in accurately describing system behavior through mathematical models such as a parametric dynamic model. In this paper, a modified differential evolution (MDE) algorithm is proposed to identify the parametric dynamic model of a Pendubot system with friction. In the MDE algorithm, the improvement is to focus on the mutation phase with a new mutation scheme in which multi-mutation operators are used, including rand/1 and best/1 for selecting target vectors in population. The performance of the MDE algorithm is tested on a set of fourth benchmark functions, and it is compared with the other algorithms such as a traditional differential evolution (DE), a hybrid DE (HDE) algorithm and a particle swarm optimization (PSO). The MDE algorithm is then used to identify the Pendubot' parameters accurately. Experimental results demonstrate the high performance of the proposed method regarding robustness and accuracy.