The paper presents a tuning methodology for the parameters of adaptive current and speed controllers in a permanent magnet Brushless DC (BLDC) motor drive system. The parameters of both inner-loop and outer-loop PI controllers, which vary with the operating conditions of the system, are adapted in order to maintain deadbeat response for current and speed. Evenly distributed operating points are selected within the preset regions of system loading. A Particle Swarm Optimization (PSO) algorithm is employed in order to obtain the controller parameters assuring deadbeat response at each selected load.

The Brushless DC (BLDC) motor is a permanent magnet synchronous machine supplied from a six-transistor inverter whose on/off switching is determined by the rotor position (Krishnan, 2000; and Chiasson, 2005). There are no brushes or commutator. The system is becoming increasingly attractive in high performance variable-speed drives since it can produce torque-speed characteristic similar to that of a permanent-magnet conventional DC motor while avoiding the problems of failure of brushes and mechanical commutation. In addition to the reduction in maintenance, the BLDC motor has low inertia, large power-to-volume ratio, and significant reduction in friction and noise as compared with the permanent-magnet conventional DC servo motor at the same output rating. However, all the above advantages are considered at the price of high cost and more complex controller than that of the conventional motor. Good armature current response is indispensable if BLDC motors are to have satisfactory driving performance. Many current control techniques have been developed, e.g., vector control (Dai et al., 2003), predictive control (Oh et al., 1999), deadbeat control (Bose, 2011), and direct torque control (Yong et al., 2005). Each approach has its own advantages and limitations. Classical controllers, however, suffer from the variation of electrical machine parameters such as armature resistance. The electrical parameters frequently vary with driving conditions, e.g., variation of temperature and saturation phenomenon. Furthermore, possible variable loads result in more severe parameter variations. Coupling the mechanical load to the motor shaft may cause variation of inertia and viscous friction coefficients. The main control objective is to force the speed and/or current of a BLDC motor to follow its reference trajectories. With such parameter uncertainties, serious performance deviations will occur. Therefore, the robustness against machine parameter variations is an important issue to keep continual control performance all the time.

Electrical and Electronics Engineering Journal, Brushless DC motors, Adaptive control, Deadbeat response, Particle Swarm Optimization.