DEVELOPMENT AND DEMONSTRATION OF A NEW CLASS OF ADAPTIVE PARTIAL STATE-FEEDBACK CONTROLLERS FOR ELECTRIC MACHINES

The design and implementation of a family of adaptive, partial state f eedback controllers are presented for the nonlinear, third-order model commonly used to represent the permanent magnet stepper (PMS) motor, the brushless D.C. (BLDC) motor, or the switched reluctance (SR) motor driving a position dependent load. Specifically, despite parametric u ncertainty, the controllers achieve rotor position tracking via measur ements of rotor position and stator current. The adaptive control is b ased on the use of a dynamic filter to generate a rotor velocity-relat ed signal, gradient-type update laws to compensate for parametric unce rtainty, and nonlinear damping control terms to null out the effects o f unmeasurable signals. The control design technique is fully detailed for the PMS motor and includes the development of the closed-loop err or systems and a proof of global asymptotic position tracking. The des ign approach is then reformulated for the BLDC and SR actuators. Final ly, the controllers are applied to off-the-shelf industrial motors to demonstrate the effectiveness of the adaptive control scheme. Copyrigh t (C) 1996 Elsevier Science Ltd.