Auto-Regressive Exogeneous Structure Based Predictive Torque Control of Induction Motor Drive With Improved Flux Estimation

The accuracy of conventional predictive torque control (CPTC) of an induction machine relies on precise machine parameters and combinatory estimation of these parameters is tedious since they are coupled and continuously varying with operating conditions. To make control robust against imprecise models and variations in machine parameters, autoregressive exogenous structure-based predictive torque control (ARX-PTC) is presented in this work. ARX structure is formed by relating discrete time input and output samples using a linear difference equation, in which coefficients are tuned online with the help of the recursive least square (RLS) method. Adaptive dual second order generalized integrator (DSOGI) along with gain normalized improved frequency locked loop (IFLL) is employed for precise flux estimation that nullifies the issues of DC drift and harmonics present in sensed signal. To accurately track frequency ramp references (acceleration and deceleration mode), an improved structure of FLL is used. The presented control algorithm is simulated in Matlab/Simulink platform and effectiveness of control is verified with the experimental results.