Simulation-Based Parameter Identification of DC Motor Using the Equilibrium Optimizer Algorithm

Accurate identification of electric-motor parameters are essential for developing reliable control systems, because designing controllers for permanent-magnet DC (PMDC) motors requires prior knowledge of the motor model parameters. Conventional identification methods often suffer from slow convergence and reduced precision when the motor operates under nonlinear conditions. This paper proposes a simulation-based parameter-estimation method for PMDC motors that employs the Equilibrium Optimizer (EO), a physics-inspired metaheuristic algorithm based on dynamic mass-balance principles. The identification problem is formulated as minimising a cost function combining current and angular-velocity errors measured from step voltage excitation, and steady-state relationships are used to reduce the number of parameters that must be estimated. Simulation results show that the EO-based method converges quickly and produces parameter estimates with errors consistently below 0. 3%, yielding lower root-mean-square errors than the Stieglitz-McBride (CM) and Cuckoo Search (CS) algorithms. These findings highlight the potential of equilibrium-based optimisation for precise, stable and computationally efficient PMDC motor parameter identification.