The application of adaptive control to an electrical machine with unpredictable load conditions.

This study investigates the application of adaptive time-suboptimal positional control to an electrical machine with a wide range of loading conditions. These unpredicatable load conditions included variable system parameters, such as inertia variations and nonlinear amplification gain in the servo driver, as well as external disturbances, including viscous frictional force, coulomb frictional force and static loading torque. The design objective was to provide an extremely fast positional movement to the desired target without overshoot and zero steady-state error over these loading conditions. The resultant microcontroller-based adaptive controller consists of an on-line parameter estimator and a robust time-(sub)optimal position controller. The system parameters are estimated by an recursive least squares (RLS) estimator during the acceleration phase. The sampling frequency used by the RLS algorithm is determined adaptively. During the crusing phase of the positional movement, the estimates are further improved by feeding intersample data (stored during the acceleration phase) through an off-line RLS estimator. The coulomb friction and the static loading torque are effectively compensated by a simple mechanism. Another novel mechanism which takes account of nonlinear amplifier gain has also been developed. The time-(sub)optimal position controller calculates the desired reference trajectory in real-time and directs the system state to the reference trajectory. The above adaptive control scheme was implemented on a microcontroller-based system and was applied to an experimental system consisting of a 500W DC permanent magnet motor fed by a pwm servo driver. Experimental results revealed that the proposed controller adapted well to changes in inertia, viscous friction, coulomb friction and amplifier nonlinearity, and the desired time-suboptimal respones were obtained in all these loading conditions