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Abstract In process industries, closed-loop step and closed-loop relay feedback tests are popularly used for estimating model parameters. In this paper, different methods available in the literature for parameter estimation using conventional techniques and techniques based on relay feedback test are surveyed by reviewing around 152 research articles published during the past three decades. Through a comprehensive survey of available literature, the parameter estimation methods are classified into two broad groups, namely conventional techniques and relay-based parametric estimation techniques. These relay-based techniques are further classified into two subgroups, namely single-input-single-output (SISO) systems and multi-input-multi-output systems (both square and nonsquare), and are revealed in a lucid manner with the help of benchmark examples and case studies. For the above categorized methods, the procedural steps involved in relay-based parametric estimation methods are also presented. To facilitate the readers, comparison tables are included to comprehend the results of different parametric estimation techniques available in the literature. The incorporation of quantitative and qualitative analysis of papers published in various journals in the above area with the help of pie charts and graphs would enable the readers to grasp the overview of the research activity being carried out in the relay feedback domain. At the end, the challenging issues in relay-based parametric estimation methods and the directions for future investigations that can be explored are also highlighted.

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In this article, the design of brushless DC (BLDC) motor is performed using multiobjective optimization algorithm (MOOA) by satisfying multiple objectives. Initially, sensitivity analysis is carried out to find the most influencing parameters that affect the performance of BLDC motor. MOOAs such as Pareto envelope-based selection algorithm (PESA), Pareto archived evolution strategy (PAES) and nondominated sorting genetic algorithm-II (NSGA-II) is employed in the optimal design of the BLDC motor. The proposed MOOA have three objectives namely: output torque maximization, volume minimization, and minimization of total losses. MOOAs are analyzed using performance metrics and qualitative comparison is provided to select the best algorithm. Later, finite element method (FEM) is used to investigate the transient and thermal characterization on the BLDC motor designed using NSGA-II. The thermal results thus obtained using NSGA-II for the above motor under different operating conditions is also compared with the existing single objective optimization algorithm. From the comparisons, it is observed that NSGA-II algorithm significantly outperforms the existing single objective optimization algorithm. Finally, the usefulness of the designed machine based on NSGA-II is compared with the results obtained from simulation and hardware analysis.

In the design of electrical machinery, direct search methods of conventional optimization techniques are widely used. In the recent years, Genetic Algorithm (GA) has been successfully applied to the design optimization of electromagnetic devices and electrical machinery. In this paper, an optimal design method to optimize the cost of three phase Induction Motor (IM) is presented. The motor design procedure consists of a set of non -linear equations, which imposes IM characteristics, motor performance, magnetic stresses and thermal limits, and also checks that certain constraints imposed are not violated. Optimization has been carried out using a conventional optimization method, Rosenbrock's method and also using GA. A combination of conventional optimization technique with GA (i.e., results obtained in Rosenbrock's method are fed as input data in GA process) has also been carried out. A comparative study and analysis of the end results obtained through computer simulations are presented in the paper.