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Metaheuristic optimization approach has become the new framework for control synthesis. The main purposes of the control design are input (command) tracking and load (disturbance) regulating. This research proposes an optimal proportional-integral-derivative (PID) controller design for the brushless dc (BLDC) motor speed control system with input tracking and load regulating constrained optimization by using the cuckoo search (CuS), one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time, the maximum overshoot, settling time and steady-state error are set as inequality constraints for tracking purpose, while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CuS will be compared with those obtained by the particle swarm optimization (PSO) and genetic algorithm (GA). From simulation results, it was found that the parameters of CuS is optimal than both PSO and GA for BLDC motor speed control system satisfying the preset input tracking and load regulating constraints. In addition, the simulation results are confirmed by the experimental ones from the BLDC motor speed control system developed in real laboratory.
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