Switched Reluctance Motor Sensorless Fed by Photovoltaic System Based on Adaptive PI Controller

The purpose of this paper is to investigate the capabilities and to compare the performance of two popular 8/6 pole and 10/8 pole switched reluctance motor (SRM) for electric vehicle and hybrid electric vehicle applications. This article glimpses an overview of Solar Photovoltaic (PV) and a battery-based inverter topology for a 7.5kW rated SRM drives with 8/6 pole and 10/8 pole. A closed loop hysteresis current control for both the type of SRM motor is implemented. The simulation results of both the SRM drives are presented for variable speed and motor acceleration. This paper also presents the performance indices of battery, Solar PV, and a boost converter operated with perturb and observe (P&O) maximum power point tracking (MPPT) algorithm using with change in solar irradiation. Both the SRM drive systems are simulated in MATLAB/Simulink environment and comparative results presented.

In this paper, a control mechanism for speed control of switched reluctance motor (SRM) with torque ripple reduction utilising moth flame optimisation algorithm and genetic algorithm with recurrent neural network (CMFG-RNN) is presented. The control mechanism comprises of FOPID speed controller in the external loop and current controller in the internal loop along with control of turn-on and turn-off angles for the SRM. The issue of getting the ideal estimations of proportional, integral and derivative gains for both speed and current controller along with the turn-on and turn-off angles is considered as a multi-objective optimisation problem with the goals of limiting the Integral Squared Error of speed and torque ripple. Simulations of CMFG-RNN-based control of SRM are done utilising SIMULINK/MATLAB software. With a specific end goal to assess the strong execution of CMFG-RNN, the comparison of current, speed, flux and torque under different techniques are considered. The outcomes got by CMFG-RNN are contrasted and validated with ALO and NSGA-II techniques. The outcomes uncover that CMFG-RNN-based controllers give better execution as far as lesser torque ripple and quick settling time because of its systematic random search capabilities, thereby enhancing the dynamic execution of SRM drive.

The switched reluctance motor drive (SRD) has obtained great attention as an AC stepless speed control system due to its large regulating scope, low cost and ruggedness. However, its strong nonlinearity and multivariable characteristic make it difficult to control. To solve the problem, this paper presents an approach of adaptive PWM speed control for switched reluctance motors (SRM) based on RBF neural network. This method builds up a speed controller based on RBF neural network which has powerful approximating ability and fast convergence property. The controller is trained off-line in advance, and then with the motor's operation, the on-line training of it makes its parameters vary with the environment in order to improve the control performance. In addition, another RBF network is constructed to offer gradient parameters, which is needed by the on-line training, via on-line identification. The results of experiments prove that the approach has lots of advantages in response speed, control accuracy and adaptability.

Switched Reluctance motor (SRM) are accepting critical consideration from industries, due to its straight forward structure; low-cost manufactur ability and dependability make it better than other electric machines. For adjustable speed tasks, the drive of SRM is most applicable. Additionally, the performance of the current and speed controller for SRM driver framework can be negatively influenced by noise, disturbances, and inactivity of load torque. To solve this difficulty, this paper developed an ANFIS based speed and current controller for SRM. The key objective of this article is to obtain the preferred speed and current performance of switched reluctance motor. The Neuro-fuzzy structure includes the assistance of both neural-systemand fuzzy scheme. Adaptive Neuro-Fuzzy Inference System(ANFIS)controller is more efficient than the neural system and fuzzy logic-based controller. In designed ANFIS method for speed and current control of SRM, the neural network methodology is applied for a selection of suitable rule base with the support of the back propagation (BP) algorithm. Finally, the experimental outcomes show that the effectiveness in the aspects of better speed and current performance compared to other controllers.

A unified control method combined with improved TSF and LADRC for SRMs Using modified grey wolf optimization algorithm

This paper studies the speed control strategy of a switched reluctance motor based on angle-position control (APC). The switched reluctance motor has three control parameters: turn-on angle, turn-off angle and voltage PWM duty cycle. This paper studies the function of the three parameters and designs the control algorithms of the parameters, respectively, which can reduce the coupling degree, simplify the control process, and realize the optimal control of the switched reluctance motor. By studying the nonlinear characteristics of the switched reluctance motor, the optimal current waveform in the effective working range of the inductor is obtained, and then a control strategy of the turn-on angle is designed to realize the ideal winding current waveform. According to the torque characteristics of the motor, taking the coincidence of the freewheeling zero point and the position angle at the end of the maximum inductance interval as the control target, a control strategy for the turn-off angle that makes full use of the effective inductance working interval is proposed, which improves the efficiency of the system. For the nonlinear and time-variant switched reluctance motor running process, a data-driven model-free adaptive control algorithm is introduced, and a switched reluctance motor speed control algorithm based on voltage PWM duty cycle is designed. The main contribution of this paper is to propose a control strategy that is generally applicable to switched reluctance motors, which does not depend on the precise mathematical model of the motor. The control algorithms are designed separately for the three control parameters according to the characteristics of the motor, which reduces the degree of coupling among them. A switched reluctance motor drive system based on angle-position control is designed. This strategy is especially suitable for driving the load with sudden large torque pulsation.

In this paper, a speed controller with FNN (feedforward neural network) is proposed for motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. An FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, an FNN is introduced to convert the discontinuous input to a continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR (switched reluctance) motor which has high nonlinearities and it is compared with MRAC (model reference adaptive controller). Experiments on the SR motor illustrate the validity of the proposed controller.

In this paper, the topology of a single switch, single input and dual output SEPIC (Single Ended Primary Inductance Converter) is utilized for proposed SPV (Solar Photovoltaic) based water pumping system employing SRM (Switched Reluctance Motor). The proposed DC-DC converter has advantages of conventional SEPIC including capability to provide the efficient voltage balancing of DC link capacitors of a mid-point converter of SRM. Therefore, it eliminates the need of extra efforts to balance the DC link voltage across DC link capacitors and makes the proposed water pumping system cost effective and simple. A unique approach of variable DC link operation of SRM drive with an electronic commutation for switches of a mid-point converter is used with the elimination of current sensors at motor side which results in reduction in cost and losses of the system. The characteristic of providing dual output voltages with no polarity reversal and having features like minimal active components and low noise operation makes proposed dual output SEPIC suitable for solar water pumping using SRM drive. For increasing the efficiency of pumping system, an incremental conductance (InC) based MPPT technique is used to feed a SRM drive.

In this paper, the global adaptive speed tracking control algorithm with accurate parameters estimation of the surface mounted permanent magnet synchronous motors is presented. The adaptive controller is designed using second Lyapunov method and does not require over-parametrization. The algorithm may be used for fast self-commissioning of all motor parameters as well as adaptive speed controller with online identification of some parameters. During self-commissioning, in the first stage the stator resistance and inductance are estimated in motionless mode. Second stage provides identification of all mechanical parameters in speed control mode. Simulation results proof the effectiveness of the theoretical findings.

There are three types of motors used in electric vehicles currently, namely permanent magnet synchronous motors, induction motors, and Switched Reluctance Motors (SRM). SRM has its advantages over other types of motorcycles. The advantages of SRM are weight, reliability, fault tolerance, and acceleration time. However, the SRM motor needs to be adjusted to have the required speed and transient response. This study aims to simulate and design speed control in SRM. The Proportional Integral (PI) control simulation is firstly conducted by using MATLAB to see the results of the control before it is implemented in SRM. From the results of the SysId Matlab, it is obtained system transfer function in the first order. The transfer function of this system is used to determine the value of Kp and Ki analytically obtained the value of Kp is 0.006159 and Ki is 0.054752. SRM operates at PWM 72-84 or in a duty cycle of 28.23% - 32.94%. This PWM signal is equivalent to the driver input voltage of 1.446 Volts - 1.756 Volts. The implementation is conducted by using an Arduino microcontroller and it only uses a positive control signal that makes the motor rotate in only one direction. From the simulation and implementation that were carried out, it is found that the control system that has been designed is able to track the speed at the setpoint, namely 400 rpm, 600 rpm, and 800 rpm. The settling time, respectively, is 1.2 s, 2 s, and 1.3 s, overshoot is 52.75%, 62.16%, and 13.87%. The steady-state error for each setpoint is 10%, 0.33%, 0.5%.

This paper aims at the design and development of an efficient photovoltaic (PV) array powered switched reluctance motor (SRM) driven pump utilizing a dual supply buck–boost converter. The buck–boost converter, having dual supply, is used as an intermediate dc–dc power conditioning stage between PV array and SRM-pump. To optimize the power of the PV array and to provide soft starting to the SRM drive are the two major concerns of this converter. It also provides voltage balancing of split capacitors, owing to which there is considerable reduction in torque ripple and the required size of it. Moreover, the continuous conduction mode operation of dual supply dc–dc converter minimizes current and voltage stresses on converter indices and reduced electromagnetic interference. The electronic commutation of the SRM drive at fundamental frequency offers reduced switching losses in the midpoint converter and, therefore, considerable enhancement in the efficiency of the proposed system. A dwell angle self-starting control algorithm for a four-phase 8/6 SRM is also implemented in the proposed system. A prototype is developed in the laboratory to validate the performance of the proposed system in different environmental conditions.

This paper aims at developing an inexpensive autonomous photovoltaic (PV) array fed irrigation pump utilizing the switched reluctance motor (SRM) drive. A modified Zeta converter is utilized between PV array and motor-pump to incorporate power conditioning. Its continuous conduction mode (CCM) operation helps to reduce the stress on its devices and also the size of PV capacitor. A Perturb and observe (P &O) maximum power point tracking (MPPT) control algorithm is used to maximize the power output of the PV array. DC link voltage is kept variable which allows reduction of sensors. The fundamental switching of mid-point converter improves the efficiency of the system. The suggested system is premeditated in MATLB and further confirmed with hardware implementation. The simulated and hardware results under varying environmental conditions testify the utility and worthiness of proposed system for irrigation pumps.

Torque ripple is a major problem for switched reluctance motors, which may cause undesirable vibration and acoustic noise, especially at low speed. This paper presents an optimal topology structure of rotor for 8/6 pole switched reluctance motor by using the level set method. The nonlinear ferromagnetic material boundary of rotor pole is implicitly represented through an embedded level set function. This method is applied to obtaining the optimal distributions of material in the design domain for minimizing torque ripple and maximizing average torque. In the optimization objective function, the reluctivity of ferromagnetic material is selected as the design variable. The normal velocity is derived from sensitivity analysis, where the adjoint variable method is utilized. Two-dimensional finite element method is employed to calculate the electromagnetic parameters of this electric machine. Analysis results obtained from the transient simulation of electromagnetic field coupled with control circuit show that the torque ripple is reduced effectively by using the optimized rotor structure.

In this manuscript, a hybrid technique is proposed for Torque Ripple (TR) minimization of Switched Reluctance Motor (SRM). The proposed technique is the consolidation of Wingsuit flying search (WFS) optimization and Gradient Boosting Decision Tree (GBDT) algorithm, hence it is known as WFS-GBDT technique. The control mechanisms consists of fractional order proportional integral derivative (FOPID) speed controller on external loop as well as current controller on internal loop with controlling turn activate and deactivate angles for SRM. The complexity of acquiring the ideal evaluation of proportional, integral and derivative gains for speed and current controller including turn activate and deactivate angles are deemed as a multi-objective optimization issue. Here, the WFS optimize the gain parameters of external speed loop along internal current loop with commutation angles for turn activate and deactivate switches. The WFS optimization processing is used to productive machine learning dataset under the types of SRM parameter. By using the satisfied dataset, the GBDT is predicted and mandatory forecasting is implemented in the entire machine operating stage. The optimized gain parameters based, the fractional order proportional integral derivative controller is tuned exactly. The proposed WFS-GBDT control technique lessens the torque ripple and quick settling time with this proper control, because of its systematic random search capabilities, thereby enhancing the dynamic execution of SRM drive. Finally, the proposed technique is activated in MATLAB/Simulink site, its performance is analyzed with existing techniques, like Base, ALO and WFS. The best, worst, mean, standard deviation for ISEspeed using proposed technique attains 230.5364, 231.5934, 230.952 and 0.05314. The best, worst, mean and standard deviation for torque ripple using proposed technique attains 0.4571, 0.6548, 0.585 and 0.472. The best, worst, mean, standard deviation for ISEcurrent using proposed technique attains 3.1257, 3.9754, 3.5783 and 0.0472.

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme.