Adaptive Neural Controller for Speed Control of PMSM with Torque Ripples

Pajchrowski, Tomasz; Siwek, Przemyslaw; Wójcik, Adrian

doi:10.1109/pemc51159.2022.9962877

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…The neural network can be used as an efficient torque ripple compensator [61]. Because the number of PMSM-based drives in the industry is increasing, further development of solutions similar to those presented in [62,63] is expected. In addition to implementing neural networks as controllers, applications of the so-called hybrid controllers has become a future solution.…”

Section: Neural Controllersmentioning

confidence: 99%

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

Kaminski

Tarczewski

2023

Energies

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Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

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Section: Neural Controllersmentioning

confidence: 99%

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

Kaminski

Tarczewski

2023

Energies

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“…Neural networks are becoming a common tool used in today's control theory. Apparently, neural networks are a widespread solution used in the field of electric drives as well [37][38][39]. Considering the fact that a novel electric drive system demands a quick response for each disturbance or possible change of the parameters, it is crucial to match the used model to the current application.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Kaczmarczyk,

Stanislawski,

Szrek

et al. 2024

Energies

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External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

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“…Load torque must be treated as an external disturbance; therefore, establishing a tool to precisely assess its value at all times is a very difficult task [30]. Intelligent structures with neural networks are widely investigated in the modern literature [31][32][33][34][35]. Neural networks can be utilized in electric drives for multiple purposes, such as state variable estimation [36], motor condition monitoring and diagnosis [37], enabling control with damaged sensors (often referred to as Fault-Tolerant Control) [38], and adaptive control [39].…”

Section: Introductionmentioning

confidence: 99%

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

2023

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Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, Radial Basis Function neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.

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Adaptive Neural Controller for Speed Control of PMSM with Torque Ripples

Cited by 3 publications

References 27 publications

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

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

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

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