Nature-Inspired Algorithm Implemented for Stable Radial Basis Function Neural Controller of Electric Drive with Induction Motor

Abstract

The main point of this paper was to perform the design process for and verify the properties of an adaptive neural controller implemented for a real nonlinear object—an electric drive with an Induction Motor (IM). The controller was composed as a parallel combination of the classical Proportional-Integral (PI) structure, and the second part was based on Radial Basis Function Neural Networks (RBFNNs) with the on-line recalculation of the weight layer. The algorithm for the adaptive element of the speed controller contained two parts in parallel. The first of them was dedicated for the main path of the neural network calculations. The second realized the equations of the adaptation law. The stability of the control system was provided according to the Lyapunov theorem. However, one of the main issues described in this work is the optimization of the constant part of the analyzed parallel speed controller. For this purpose, the Grey Wolf Optimizer (GWO) was applied. A deep analysis of the data processing during the calculations of this technique is shown. The implemented controller, based on the theory of neural networks, is an adaptive system that allows precise motor control. It ensures the precise and dynamic response of the electric drive. The theoretical considerations were firstly verified during the simulations. Then, experimental tests were performed (using a dSPACE1103 card and an induction machine with a rated power of 1.1 kW).

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

“…With this assumption, the calculations are simple, but only a few parameters shape the control signal. An extension of the above solution is a controller based on an MLP neural network with sigmoidal activation functions [57] or the implementation of a radial basis function neural network where an additional adaptation of the centers, apart from the weights, is applied [58]. It should be noted that these models process data in one direction.…”

Artificial Intelligence in Electric Machine Drives: Advances and Trends

“…The hardware experiments further reveal that when compared with the PI controllers, the ANN-based controllers can achieve much better current tracking performance with a low PWM switching frequency of 4 kHz, which further yields possibilities to improve the motor drive efficiency by lowering its switching loss. A more recent study that also runs on the dSPACE DS1103 card develops a controller that is composed as a parallel combination of the classical PI structure and the radial basis function neural network [100].…”

Memoryless radial basis function neural network based proportional integral controller for PMSM drives

“…Radial basis function neural network (RBFNN) is one of various neural network methods that has been studied thoroughly and applied to control many drive systems in recent year [24]- [26]. RBFNN evinces its innate characteristics of simple architecture, fast learning rate, and have better approximation abilities.…”