Development of an adaptive neuro-fuzzy controller for speed control of induction motor drives

Abstract

Robust and precise speed control is of critical importance in high performance drive applications. Control schemes for AC machines are complicated as compared to DC machines since they exhibit nonlinear relationships between process variables e.g. speed and manipulated variables e.g. current, torque etc. Direct torque control (DTC) scheme offers faster and simpler control of AC machines with high dynamic performance but without extensively using coordinate transformations and hence with lesser computational burden on the processor. Artificial intelligent controllers (AIC) are capable of overcoming the limitations of the mathematical model dependent conventional fixed gain and existing adaptive controllers. In this thesis, an adaptive neuro-fuzzy inference system (ANFIS) based controller is proposed to improve the dynamic behavior of DTC based induction motor drive which offers the combined advantages of the flexibility of fuzzy logic and adaptability of neural networks. Hence the developed adaptive neuro-fuzzy controller can be utilized to minimize the effects of unavoidable system disturbances such as, system parameter variations, sudden impact of load changes etc. Since the use of intelligent controllers are very limited in the drives industry due to the relevant computational burden on the microprocessor, therefore, in order to reduce the computational burden, linear linguistic variables with an optimum number of membership functions have been selected for the adaptive neuro-fuzzy controller developed in this thesis. The effectiveness of the proposed NFC based DTC scheme of the IM drive is consolidated through the development of a simulation model using MATLAB/Simulink. The results obtained from the simulation of the proposed system are compared with the results from simulation of the same system using conventional proportional integral (PI) controller. The results have shown promising improvements both in the transient and steady state responses of the system. Further, as an integral part of the thesis, a fuzzy logic controlled dynamic voltage restorer (DVR) system has been developed in order to protect the induction motor drive system from the power quality problems (like- voltage sag, swell etc.). The DVR system was developed and simulated in MATLAB/Simulink environment to verify its functionality. The simulation results have confirmed the ability of the DVR system to perform at an expected level when used in conjunction with the induction motor drive. The DVR system kept the induction motor drive performance intact under both symmetrical and asymmetrical system fault conditions.