Adaptive multi-layer self-tuning high performance tracking control for DC brushless motor

Adaptive control for high performance drive systems has become an important subject of research. In applications such as robotics, actuation, and manipulation, the rotor of the electric motor should follow a pre-selected track at all time. The tracking accuracy should not be affected by parameter uncertainties, unknown load variations, or sudden external disturbances. In this paper two schemes of adaptive control are developed and tested for a DC brushless motor. The first scheme is a single-layer self-tuning controller based on the generalized minimum variance theory. The second is a multi-layer adaptive controller consisting of a self-tuning control layer and a supervisory control layer. The supervisory controller continuously monitors the status of the system parameters, the structure of the controller, and the motor performance. A laboratory setup is constructed to test the proposed methods. Laboratory results show that the multi-layer controller is capable of achieving the tracking process with a high degree of accuracy, even in the presence of large and sudden disturbances