Application of recent nature-inspired meta-heuristic optimisation techniques to small permanent magnet DC motor parameters identification problem

In this study, an attempt is made to find an effective solution on the small direct current (DC) motor's parameters identification problem by employing two recently introduced stochastic nature-inspired techniques. The first one is called 'salp swarm algorithm' (SSA) while the second one is called 'ant-lion optimiser' (ALO). These algorithms have been inspired by the interaction strategy between the ocean salps and the ants and ant-lions, respectively, in nature. To appraise the effectiveness of these algorithms, a DC motor model has been appropriately implemented and its performance is evaluated by using speed step responses, while all model's parameters are considered unknown and therefore search variables. Integral of the square of the error (ISE), integral absolute error and integral in time of absolute error have been adopted as objective functions for the algorithms' evaluation. In order to judge the acceptability of these algorithms, the simulation results are compared with those of another one similar technique, namely the 'grey wolf optimiser' (GWO). The obtained results reveal very satisfactory performance and confirm that the examined SSA, ALO and GWO algorithms can identify accurately the DC motor parameters and can be applied effectively to the specific problem. Another finding is that SSA combined with ISE criterion seems to be the most appropriate technique among the three algorithms.