Accurate Identification and Simulation of Brushless DC Drive Actuating System for High Performance Applications
Mohammed Ahmed, Luis Manuel Vaca Benitez, Frank Kirchner
In Proceedings of The 4th International Industrial Control and Automation Technology Exhibition and Conference, (Automation-2010), 10.5.-12.5.2010, Cairo, o.A., May/2010.
Brushless DC (BLDC) motors are widely used for high performance industrial applications such as in the areas of supervised actuation in aerospace and guided robotic manipulations, numerically controlled machine tools, and hybrid vehicles where high torque and precision control are required. This is mainly due to their wide bandwidth speed and torque control loops, compactness, high torque-to-weight ratio, and virtually maintenance free operation by comparison with conventional DC types. Furthermore, for high torque applications, BLDC motors current control is accomplished through pulse width modulation (PWM). This efficient method of power transfer provides a wide range of continuous power output in servo-amplifier operation. These drive systems, in which fast response insensitivity to parameter variations, quick recovery of speed from load impact are of critical importance, are usually controlled with conventional PID controllers. Consequently, proper selection and optimization of the PID parameters is necessary to avoid significant overshoot and oscillations in precision control applications. Tuning of these controllers is dependent on accurate models of the electromechanical system. Furthermore accurate modeling is also an issue in feasibility studies concerning system performance prediction and evaluation, via simulation, where new embedded drive systems are proposed. It is quite difficult to obtain an accurate system model for a physical process in terms of first principles or known physical laws as such models, in most cases, will be overly complex or even impossible to obtain in reasonable time. The complicated white-box modeling is due to the complex nature of many systems and processes and because of the unknown conditions such as saturation disturbances, parameter drifts, and noise which are unavoidable and cannot be mathematically expressed accurately. Therefore, system identification is a much more common
approach to start from measurements of the behavior of the system and the external influences on it and try to determine a mathematical relation between them without going into the details of what is actually happening inside the system.