Modeling and control strategies for a variable reluctance direct-drive motor

In industrial automation and robotic applications, direct-drive motors represent a suitable solution to friction and backlash problems typical of mechanical reduction gears. Variable reluctance (VR) motors are well suited for direct-drive implementation but, because of the strongly nonlinear electromechanical characteristics, these motors are traditionally designed as stepper motors. The main aim of the work described in the paper is the design of a high-performance ripple-free dynamic torque controller for a VR motor, intended for trajectory tracking in robotic applications. An original modeling approach is investigated in order to simplify the design of the high-performance torque controller. Model structure and parameter estimation techniques are presented. Different approaches to the overall torque controller design problem are also discussed and the solution adopted is illustrated. A cascade controller structure is considered. It consists of a feedforward nonlinear torque compensator, cascaded to a nonlinear flux or current closed-up controller. The feedforward compensator is carefully considered and optimization techniques are used for its design. Two optimization criteria are proposed: the first minimizes copper losses, whereas the second minimizes the maximum value of the motor-feeding voltage. Although developed for a specific commercial motor, the proposed modeling and optimization strategies can be used for other VR motors with magnetically decoupled phases, both rotating and linear. Laboratory experiments for model validation and preliminary simulation results of the overall torque control system are presented.