Estimating Single Layer Bi-Channel Neural Networks Architecture for Speed Control of Variable Reluctance Motors

This study explores the speed adjustment characteristics of the adaptive control of estimated single-layer Bi-channel neural networks (NNs) for Variable Reluctance Motor (VRM) drives. The innovative algorithm incorporates an estimation method to manage the nonlinear behavior of a VRM through a collection of Bi-channel NNs nodes. Each node acts as a single-layer Bi-channel NNs controller at a local linear operating point extending across the nonlinear surface of the system. Although this algorithm demonstrates strong tracking performance, a significant challenge arises from the motor speed being integrated into the machine model, which means that the controller is directly influenced by speed variations. This results in a sluggish speed adjustment response due to the learning process involved. To address this challenge, a grid of NNs must update the NNs-matrices whenever rotational speed changes. Finally, a simulation of the proposed novel speed control has been conducted to illustrate the speed adjustment behavior of the VRM under various operating conditions and to validate the effectiveness of the control approach.