The online parameter identification of chaotic behaviour in permanent magnet synchronous motor by Self-Adaptive Learning Bat-inspired algorithm

•This paper introduces the new online identification of nonlinear systems.•The proposed framework is simple and does not have complexities.•The proposed is based on a Self-Adaptive Learning Bat-inspired Optimization algorithm.•The reflection of a chaotic behavior as the PMSM is positioned in a particular area. One of the main issues in engineering is the identification of nonlinear systems. Because of the complicated as well as unexpected behaviours of these chaotic systems, it is introduced as special nonlinear systems. A minute change in the primary conditions of such systems would lead to significant variations in their behaviours. On the other hand, due to simple structure of Permanent Magnet Synchronous Motors (PMSM) and its high applications in industry, the use of this machine is dramatically increasing these days. The reflection of a chaotic behaviour as the Permanent Magnet Synchronous Motor is positioned in a particular area. In the model of PMSM, the exact parameters of the system are required to properly control and spot the error. In this paper, Self-Adaptive Learning Bat-inspired Optimization algorithm is used for solving both offline and online parameter estimation problems for this chaotic system. In addition, noise is considered as one of influential factors in control of PMSM. According to simulation results, it can be claimed that the proposed algorithm is a very powerful algorithm for online parameter identification for PMSM.