Regression based-programmable optimal controller for induction machine

Abstract

A programmable infinite horizon controller for optimal tracking of reference speed and flux for Induction machine (IM) drives is presented. Firstly, convex optimization concept is employed to find the lagrangian type cost function. Then stator flux oriented control of IM is employed to design the controller state space model that produces unconstrained optimal input vector that minimizes the developed quadratic cost function. The regression based formulation and tuning of controller matrix is carried out for different operating points. The proposed controller is developed for a 1.5 kW Induction motor and tested in MATLAB for speed and flux tracking capability under various operating conditions. Then the stability of the controller is verified using Lyapunov's and LaSalle's invariance principle. The controller exhibited good tracking accuracies under almost all the operating conditions with a stable response better than the conventional scalar and vector control techniques.