Robust optimal control of a DC motor

Abstract

Numerical control of a DC motor is achieved by minimizing a quadratic criterion. This quadratic criterion, which is based on the state-space model of the motor and the converter, is minimized using dynamic programming. The optimal control law is given by the product of the state-space vector and an optimal feedback matrix, and the product is added to a constant term to minimize the steady-state error. The load torque is added to the state-space vector to form an augmented model to take into account torque variations. This scheme is implanted on a 5 kW DC motor, and a comparison between the pole assignment control law and the optimal control law is made. The superiority of the optimal control law over the pole assignment method is observed. The optimal control law is also tested when the inertia moment is varying, and it is shown that this scheme is robust.