Design and Implementation of a Novel Intelligent Strategy for the Permanent Magnet Synchronous Motor Emulation

Abstract

In this paper, an intelligent neural network-based controller is designed and implemented to control the speed of a permanent magnet synchronous motor (PMSM). First, the exact mathematical model of PMSM is presented, and then, by designing a controller, we apply the wind turbine emulation challenges. The designed controller for the first time is implemented on a Arm Cortex-M microcontroller and tested on a laboratory PMSM. Since online learning neural network on a chip requires a strong processor, high memory, and convergence guarantee, this article uses the offline method. In this method, first, for different work points, the neural network is trained by local controllers, and then, the trained network is implemented on the chip and used. Uncertainty in the parameters and the effect of load torque as challenges of control systems are applied in the proposed method, and a comparison with other methods is performed in the implementation results section.

Nonlinear Integrated Fuzzy Modeling to Predict Dynamic Occupant Environment Comfort for Optimized Sustainability

“…Fuzzy logic is used in various systems due to a few of its notable attributes such as not needing robust mathematics or an accurate dynamic model [21]. One of the crucial reasons why the usage of fuzzy logic has increased swiftly is that it offers the deployment of human thinking and rationale with if-then rules from the system input-output data, spawning the fundamental model structure (structural identification) and parametric identification or model training [22]. One more element of fuzzy logic when blended with fuzzy logic neural networks is dealing with uncertainty and studying the capability of the recommended model for forecasting reasons [23].…”