as the motor consumes more than 50% of total electrical energy produced in the world, the efficiency optimization of the motor is a burning issue in terms of saving energy and the environment. in modern days researchers display immense interest in the control of a high performing interior permanent magnet synchronous motors (ipmsm) drive for general industrial applications. the ipmsm is largely used in low and medium power applications such as adjustable speed drives, robotics, aerospace and electric vehicles due to its several advantageous features such as high power density, greater flux weakening capability, high output torque, high power factor, low noise, robustness and high efficiency as compared to the dc motors and induction motors (im). nevertheless, its high efficiency characteristics are influenced by applied control strategies. most of the reported works developed control algorithms for ipmsm to achieve high performance. however, the efficiency optimization of ipmsm, which is one of the important aspects is often ignored. therefore, in this thesis the efficiency optimization issues is also considered along with high performance control. this thesis presents a nonlinear loss model-based controller (lmc) for ipmsm drive to achieve both high efficiency and high performance of the drive. among numerous loss minimization algorithms (lma), a lmc approach offers a fast response without torque pulsations. however, it requires the accurate loss model and the knowledge of the motor parameters. therefore, a difficulty in deriving the lmc lies in the complexity of the full loss model. moreover, the conventional lmc does not pay attention to the performance of the drive at all. in an effort to overcome the drawbacks of conventional lmc, an adaptive backstepping based nonlinear control (abnc) is designed to achieve high dynamic performance speed control for an ipmsm drive is developed in this thesis. the system parameter variations as well as field control are taken into account at the design stage of the controller. thus, the proposed abnc is capable of maintaining the system robustness and stability against mechanical parameter variation and external load torque disturbance. to ensure stability the controller is designed based on lyapunov’s stability theory while the lmc ensures high efficiency of the drive. a neuro-fuzzy logic controller (nfc) including lmc is also developed in this work. the proposed nfc overcomes the unknown and nonlinear uncertainties of the drive, the membership function of the controller is tuned online. an important part of this work is directed to develop an adaptive network-based fuzzy inference system (anfis) based nfc. in this work, an adaptive tuning algorithm is also developed to adjust the membership function and consequent parameters. the complete closed-loop system model is developed and then simulated using matlab/simulink software. performance of the various control algorithms based ipmsm drive is investigated extensively at different dynamic operating conditions such as sudden load change, command speed change, parameter variation, etc. the performance of the proposed loss minimization based abnc and nfc are also compared with the conventional id=0 control scheme. the complete ipmsm drive have been successfully implemented in real-time using digital signal processor (dsp) controller board ds1104 for a laboratory 5 hp motor. the experimental results verify the simulation of nfc based loss minimization. it is found from the results that proposed drive algorithms can improve the efficiency by around 3% as compared to without any lma.