Purpose: The purpose of this paper is performance enhancement of ferrite-assisted synchronous reluctance (FASR) motor using multi-objective differential evolution (MODE) algorithm, considering the significant geometric design parameters.Design/methodology/approach: This work illustrates the optimization of FASR motor using MODE algorithm to enhance the performance of the motor considering barrier angular positions, magnet height, magnet axial length, flux barrier angles of the rotor and air gap length. In the optimization routine to determine the performance parameters, generalized regression neural network-based interpolation is used. The results of MODE are validated with multi-objective particle swarm optimization algorithm and multi-objective genetic algorithm.Findings: The design optimization procedure developed in this work for FASR motor aims at achieving multiple objectives, namely, average torque, torque ripple and efficiency. With multiple objectives, it is essential to give the designer the tradeoff between different objectives so as to arrive at the best design suitable for the application. The results obtained in this work justify the application of the MODE approach for FASR motor to determine the various feasible solutions within the bounds of the design.Research limitations/implications: Analysis, design and optimization of synchronous reluctance motor has been explored in detail to establish its potential for variable speed applications. In recent years, the focus is toward the electromagnetic design of hybrid configurations such as FASR motor. It is in this preview this work aims to achieve optimal design of FASR motor using multi-objective optimization approach.Practical/implications: The results of this work will supplement and encourage the application of FASR motor as a viable alternate for variable speed drive applications. In addition, the application of MODE to arrive at better design solutions is demonstrated.Originality/value: The approach presented in this work focuses on obtaining enhanced design of FASR motor considering average torque, torque ripple and efficiency as performance measures. Theposteriorianalysis of optimization provides an insight into the choice of parameters involved and their effects on the design of FASR motor. The efficacy of the optimization routine is justified in comparison with other multi-objective algorithms.

Anisotropic rotor configurations influenced by the presence of a large number of geometrical parameters in a permanent magnet assisted synchronous reluctance (PMASR) motor pose design challenges in obtaining a robust geometry satisfying the requirements of reduced torque ripple and high torque density. Therefore, the purpose of this work is to perform detailed geometrical sensitivity analysis of a 36 slot/4 pole permanent magnet assisted synchronous reluctance (PMASR) motor using h-indexing and level sensitivity analysis in order to specify a guideline for designers to prioritize the design variables for optimization. Systematic multi-level design optimization for multiple objectives is implemented by an NSGA-II algorithm aided by the finite element analysis tool, hardware prototyping and experimental validation. The optimized designs also exhibit better structural and thermal characteristics.

This paper describes Artificial Neural Network (ANN) based speed and current controller design for Permanent Magnet Synchronous Motor (PMSM).The neural network controllers are designed to translate the speed and current errors into respective driving voltage signals to the input of PMSM. A multilayer feed forward neural network is trained using Back propagation learning algorithm to estimate the driving voltage input of PMSM. To analyze the performance of neural controller, the overall system is simulated under various operating conditions. The simulation results compared with conventional P-I controller for different conditions highlight the performance of the proposed controller in steady state and transient conditions.