Development and demonstration of a new class of adaptive partial state feedback controllers for electric machines

This paper presents the design and implementation of a family of adaptive, partial state feedback controllers for the nonlinear, third-order model commonly used to represent the permanent magnet stepper (PMS) motor, the brushless DC (BLDC) motor, or the switched reluctance (SR) motor driving a position dependent load. Specifically, despite parametric uncertainty, the controllers achieve rotor position tracking via measurement of rotor position and stator current. The adaptive control is based on the use of a dynamic filter to generate a rotor velocity-related signal, gradient-type update laws to compensate for parametric uncertainty, and nonlinear damping control terms to null out the effects of unmeasurable signals. The control design technique is fully detailed for the PMS motor and includes the development of the closed-loop error systems and a proof of global asymptotic position tracking. The design approach can be reformulated for the BLDC and SR actuators. Finally, the controllers are applied to off-the-shelf industrial motors to demonstrate the effectiveness of the adaptive control scheme