Design of a robust stable speed-sensorless induction motor direct torque control system using the RBFN

The objective of this paper is design of a robust stable speed controller for speed-sensorless induction motor direct torque control (DTC) systems. Overall control systems are composed of a speed estimator and the proposed uncertainty observer using the radial basis function networks (RBFN). The induction motor systems in the real industrial fields, the obtaining of an exact mathematical model is hardly difficult due to the unmodeled uncertainties such as parametric uncertainties and external load disturbances. In this paper, the uncertainties are approximated by the RBFN, and the control algorithm is applied to the DTC system. Control laws and adaptive laws for the bounding constant and weights in the output layer of the RBFN are established so that the whole closed loop system is stable in the sense of Lyapunov. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. Simulation results show the effectiveness and validity of the proposed control algorithm.