Estimation of load torque in induction motors via dynamic sliding mode control and new nonlinear state observer

Abstract

This study conducts load torque estimation in an induction motor (IM) with uncertainty using a dynamic sliding mode controller (DSMC) that suppresses chattering using an integrator or a low-pass filter placed before the control signal of the system. Hence, the dimension of the augmented system in DSMC is larger than the dimension of the original system, thus leading to an increase in the number of system states. These new state variables should be determined to control such a system. To address this problem, a new nonlinear state observer (NSO) is suggested and utilized in this study. The proposed method is independent of the uncertainty bound of the system, but the system output must be accessible. These subjects are important in practical implementations. Lyapunov theory is applied to validate the stability of the proposed DSMC and NSO methods. Then, the boundedness of the closed-loop signals is concluded from the stability of the proposed techniques. The validity of the proposed approach is evaluated using an IM model. By using DSMC and NSO, we can simultaneously control the IM and estimate its load torque. In particular, the external bounded load torque signal is compensated by the input control signal of the motor. Simulation results illustrate the advantages of the proposed approach.