Control of variable reluctance machine (8/6) by artificiel intelligence techniques

The non-linearity of variable-Reluctance Machine (8/6) and the dependence of machine inductance on rotor position and applied current complicate the development of the control strategies of drives using variable-Reluctance Machine variable-Reluctance Machine (VRM). The classical-control algorithms for example of derived full proportional action may prove sufficient if the requirements on the accuracy and performance of systems are not too strict. In the opposite case and particularly when the controlled part is submitted to strong nonlinearity and to temporal variations, control techniques must be designed which ensure the robustness of the process with respect to the uncertainties on the parameters and their variations. These techniques include artificial-intelligence-based techniques constituted of neural networks and fuzzy logic. This technique has the ability to replace PID regulators by nonlinear ones using the human brain’s reasoning and functioning and is simulated by using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared

A combined variable reluctance network-finite element VR machine modeling for stator inter-turn short-circuit diagnosis

The work presented in this paper, proposes a comparison study between the network variable reluctance (NVR) model and the finite elements (FE) model intended for the diagnosis of stator inter-turn short-circuit (ITSC) of the variable reluctance machine (VRM). In the first place, the model of the VRM by the NRV is presented. To validate this model, the FE model of the VRM has been then studied. The detection of the ITSC is achieved by the use of the stator current spectral analysis technique. The simulation results obtained, illustrate well the interest and efficiency of the proposed model as well as of the merits of the stator current spectral analysis technique for the stator ITSC fault diagnosis of the VRM

Control techniques of switched reluctance motors in electric vehicle applications: A review on torque ripple reduction strategies

As electric vehicles (EVs) continue to acquire prominence in the transportation industry, improving the outcomes and efficiency of their propulsion systems is becoming increasingly critical. Switched Reluctance Motors (SRMs) have become a compelling option for EV applications due to their simplicity, magnet-free design, robustness, and cost-effectiveness, making them an attractive choice for the growing EV market. Despite all these features and compared to other electrical machines, SRMs suffer from some restrictions, such as torque ripple and audible noise generation, stemming from their markedly nonlinear characteristics, which affect their productivity and efficiency. Therefore, to address these problems, especially the torque ripple, it is crucial and challenging to enhance the performance of the SRM drive system. This paper proposed a comprehensive review of torque ripple minimization strategies of SRMs in EV applications. It covered a detailed overview and categorized and compared many strategies, including two general categories of torque ripple mitigation encompassing optimization design topologies and control strategy developments. Then, focused on control strategy improvements and divided them into torque and current control strategies, including the sub-sections. In addition, the research also provided an overview of SRM fundamental operations, converter topologies, and excitation angle approaches. Last, a comparison between each method in torque control and current control strategies was listed, including the adopted method, features, and drawbacks

Modelling and Control of Switched Reluctance Machines

Today, switched reluctance machines (SRMs) play an increasingly important role in various sectors due to advantages such as robustness, simplicity of construction, low cost, insensitivity to high temperatures, and high fault tolerance. They are frequently used in fields such as aeronautics, electric and hybrid vehicles, and wind power generation. This book is a comprehensive resource on the design, modeling, and control of SRMs with methods that demonstrate their good performance as motors and generators

Modelling and Control of Switched Reluctance Machines

Today, switched reluctance machines (SRMs) play an increasingly important role in various sectors due to advantages such as robustness, simplicity of construction, low cost, insensitivity to high temperatures, and high fault tolerance. They are frequently used in fields such as aeronautics, electric and hybrid vehicles, and wind power generation. This book is a comprehensive resource on the design, modeling, and control of SRMs with methods that demonstrate their good performance as motors and generators

MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data

Modeling of switched reluctance motors for torque control

Master'sMASTER OF ENGINEERIN

Neural Network Based Torque Control of Switched Reluctance Motor for Hybrid Vehicle Propulsion

Considering the extensive non-linearities in the switched reluctance motor (SRM) drive, variation in the DC bus voltage and specific requirements of the hybrid electric vehicles (HEVs) traction application, a feed-forward back propagation neural network (BPNN) based torque controller is proposed. By using proposed controller, the torque ripple has been effectively reduced at low speeds while the power efficiency has been optimized at high speeds range. The problem of multi-valuedness related with the neural network based direct inverse control has been targeted by designing a bank of two-hidden-layer neural network controllers. And the problem of torque oscillation due to the change of control mode and step change of firing angle has been solved by using dead-band filtering and nearly continuous changing of firing angle and phase currents. Computed results are presented to demonstrate the effectiveness of the proposed control scheme

Comparative analysis of switched reluctance motor control algorithms

Nowadays it has become possible to develop inexpensive modern control systems for nonlinear complexity electromechanical objects due to the development of microprocessor technology and power electronics. Switched reluctance electric machines are among these devices. It makes it possible to widely use such electric machines in various practical implementations, in particular, in traction drives, electric drives of oil and gas drilling rigs, and in other applications. The switched reluctance electric machine is a non-linear object, and its control methods require formalization and grouping. The manuscript considers the design and functional features of switched reluctance electrical machines. The main methods of controlling these electrical machine types are given. Comparative analysis of the most known methods is carried out. The main classical methods of switched reluctance electric machine control are considered, such as a relay current controller with a limitation, the method of controlling the turn on/off angles and controlling the DC link voltage. Transient responses in the electric drive system are demonstrated using the considered methods. It is shown that by adjusting the on/off angles, it is possible to reduce the torque oscillation coefficient. The identified features of the presented methods will make it possible to simplify and reduce the development time for an effective control system for switched reluctance electrical machines as well as to reduce the torque ripple