he subject of modeling and estimating of synchronous motor (SM) parameters is a challenge mathematically. Although effective solutions have been developed for nonlinear systems in artificial intelligence (AI)-based models, problems are faced with the application of these models in power circuits in real-time. One of these problems is the delay time resulting from a complex calculation process and thus the difficulties faced in the design of real-time motor driving circuits. Another important problem regards the difficulty in the realization of a complex AI-based model in microprocessor-based real-time systems. In this study, a new hybrid technique is developed to solve the problems in AI-based nonlinear modeling approaches. Through this method, the relationships among the motor parameters can be described in a linear/quadratic SM form. The most effective and modern metaheuristic methods are utilized in the creation of SM forms. The SM forms developed in this study lead to an easy design and application of the SM driver software. Therefore, a model that is faster, more effective, and more easily applicable than AI-based popular methods is developed for SMs. The proposed techniques can also be applied to many other industrial modeling problems that have nonlinear features.
Key Words: Synchronous motor, metaheuristic modeling, gravitational search optimization, linear and quadratic SM forms, parameter estimation