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).
Highlights
Over the years, induction motors have been the dominant elements of energy conversion systems.The reasons for this are the low costs of the machines, dynamic features and high reliability [1,2,3].Currently, the parameters of electric machines have significantly improved; this is the result of new technology in material fabrication and the presently popular numerical tools used for the design process
The verification of the control method, described in this paper, was performed firstly in simulations. Both parts of the parallel speed controller described in the previous sections were combined
The described neural controller was used in the speed control loop of the DFOC method
Summary
Induction motors have been the dominant elements of energy conversion systems.The reasons for this are the low costs of the machines, dynamic features and high reliability [1,2,3].Currently, the parameters of electric machines have significantly improved; this is the result of new technology in material fabrication and the presently popular numerical tools used for the design process. Induction motors have been the dominant elements of energy conversion systems. The reasons for this are the low costs of the machines, dynamic features and high reliability [1,2,3]. A significant group of this devices needs precision of work (the dynamic control of speed or position) under disturbances, a simplified design methodology and the reduction of implementation costs [4,5]. The operation of the electrical drive is often realized under the following conditions: the presence of parameter changes, measured disturbances, delays in signal processing, problems with proper identification, etc. The nonlinearity of the object is another factor that impedes the effective control of the drive
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