Sung-Hoe Huh, Kyo-Beum Lee, Ick Choy, Gwi-Tae Park, and Ji-Yoon Yoo, “Uncertainty Observer using the Radial Basis Function Networks for Induction Motor Control,” Journal of Power Electronics, vol. 4, no. 1, pp. 1–11, Jan. 2004. -SCIE > Paper

A stable adaptive sensorless speed controller for three-level inverter fed induction motor direct torque control (DTC) system using the

radial-basis function network (RBFN) is presented in this paper Torque ripple in the DTC system for high power induction motor could

he drastically reduced with the foregoing researches of switching voltage selection and torque ripple reduction algorithms However,

speed control performance is still influenced by the inherent uncertainty of the system such as parametric uncertainty, external load

disturbances and unmodeled dynamics, and its exact mathematical model is much difficult to be obtained due to their strong

nonlinearity. in this paper, the inherent uncertainty is approximated on-line by the RBFN, and an additional robust control tenn is

introduced to compensate for the reconstruction error of the RBFN instead of the rich number of rules and additional updated

parameters. Control law for stabilizing the system and adaptive laws for updating both of weights in the RBFN and a bounding constant

are established so that the whole closed-loop system is stable in the sense of Lyapunov, and the stability proof of the whole control

system is presented Computer simulations as well as experimental results are presented to show the validity and effectiveness of the

proposed system.