ANFIS Based Speed and Current Controller for Switched Reluctance Motor

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.

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.

Switched Reluctance Motor (SRM) is an electrical motor which operates by a reluctance torque that has stator and rotor salient poles. It also has a simple configuration and reasonable power electronics necessity for both AC and DC machines within adjustable-speed drives. The problem working on it is, SRM with doubly salient poles introduce a torque ripple in its output which leads to deteriorated performance on speed control. In this proposed work, torque ripple reduction and speed control of SRM is implemented, utilizing an asymmetrical converter with PI-PWM controller with the aid of Bio-inspired algorithms. By utilizing the best value of flux, torque ripple and integral square error of speed and settling times, controller design is performed. The proposed research work compares three optimization algorithms namely Crow Search Algorithm, an Improved Ant Lion Optimization Algorithm and Modified Chaotic Starling Particle Swarm technique to control the speed and torque reduction of SRM in aerospace applications. To obtain the optimal parameter values, optimization techniques are introduced and the optimized controller results are compared with other conventional controller results. In normal operation, the engine operates with two phases simultaneously, but it is modelled to meet the load specification, which is single and double phase faulty. Detection of fault gives a great outcome in control of an electrical drive system. Reduction of faulty operation time leads to better motor lifetime that is obtained by early detection of a fault. This paper presents how the five-phase switched reluctance motor is designed to meet the needs of flap actuators in mid-sized aircraft. Electrical systems are modelled to provide the right functionality at the right time in advanced aircraft. However, requirement of power for the landing gear and secondary flight control only a short duration. The system is powered on and off as required, thus preserving power. Control performance and fault analysis were verified by the results of the MATLAB simulation. Experiments have been carried out more effectively to compare the results obtained with those of simulations.

Switched reluctance motor (SRM) have strong nonlinear characteristics, and there are unknown parameter fluctuations and external load disturbances in the speed control system. In order to achieve better speed control performance, the variable universe fuzzy adaptive control algorithm is introduced as the speed controller of SRM, and the hysteresis PWM direct instantaneous torque control is introduced as the internal torque controller. Finally, the experiment proves the validity of the algorithm, which shows that the variable universe fuzzy adaptive controller can effectively improve the adaptability of SRM system to parameter fluctuation and the robustness to external load disturbance.

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.

This paper proposes a novel V/f control method for a switched reluctance motor (SRM). The proposed method does not require the rotor position information and the nonlinear magnetization curve model of SRM. A virtual rotor magnetic flux is generated by controlling the zero-phase current with a PI controller, which realizes the V/f control for SRM. In addition, the maximum torque / current ratio control is achieved by the reactive power in the V/f control. In the proposed simple speed control without a position sensor, the current RMS value is reduced by 43.9% at the torque of 0.1 p.u. compared to without high-efficiency control.

This paper presents the application of adaptive algorithm for the speed control of Switched Reluctance Motor (SRM) with load series DC machine. This system is feeding by Photovoltaic (PV) system. SRM is a highly nonlinear control plant and operates in saturation to maximize the torque output. A systematic approach to the modeling of highly nonlinear SRM drive system which includes the motor, converter, and its electronic controller is presented. Hysteresis current controlled mid-point converter is used to feed four phase, 8/6 pole SRM. Nonlinearity caused by magnetic saturation is accounted for accurate and real-time simulation of drive performance by considering experimental data of magnetization and static torque characteristics. Performance analysis of SRM drive is reported for a wide range of operating conditions via starting, reversal and load perturbation dynamics the performance indices of SRM drive system operating with adaptive controller for speed control and using the PI classical controller turn on angle for starting operation (dynamic operation). The results illustrate that the approach used are suitable for dynamic analysis and steady state of SRM fed from a PV system. It ensures smooth starting without initial hesitation and reduce the torque ripples. This proposed is explain the comparison between two technique for SRM (four phases) control system. First controller is adaptive speed control and the second controller is adaptive torque control.

Abstract-Due to nonlinear electromagnetic characteristics of Switched Reluctance Motor (SRM), as well as complexity of voltage and current waveforms, it is of great difficulty to control SRM optimally and compute power losses. A novel optimal control strategy of Non-Energy Returnable Current Chop (NERCC) was proposed, which is suitable for high power SRM drive. This method can obtain current waveform in accordance with driving mode by reasonably choosing zero-voltage status. And then power losses of SRM drive system was analyzed under Chopped Current Control (CCC) method, it is concluded that NERCC control strategy is suitable for high power SRM application. In the end, the high power SRM drive system has been on trial in coal mine, experimental results of the system confirmed that current ripple is less than 5% and good performance of the proposed strategy proved its feasibility and applicability.

The Power Factor Correction (PFC) in Switched Reluctance (SR) motor is discussed in this article. The SR motors are applicable for multiple applications like electric vehicles, wind mills, machineries etc. The doubly salient structure of SR motor causes the occurrence of torque ripples, which affects the power factor of the motor. To improve the power quality, the power factor has to be corrected and the ripples have to be minimized. In order to achieve these objectives, a novel power factor correction (PFC) method is proposed in this work. Here, the conventional Diode Bridge Rectifier (DBR) is replaced by a Bridgeless Hybrid Resonant (HR) converter, which assists in improvising the output in a wider range. The converter is chosen because of having variety of beneficial measures including high gain. The converter’s output is fed to the SR motor by means of an asymmetric Bridge Resonant (BR) converter. The proposed converter operates in continuous mode of conduction with the switching frequency of 10 KHz. A hysteresis current controller and Proportional Integral (PI) controller are utilized for reducing the harmonics in the source current along with the regulation of output voltage. In addition, the speed control of SR motor is accomplished by means of the Whale Optimization Algorithm (WOA) assisted PI controller. The proposed methodology is effective for the control of unity power factor, torque and current ripples. The Total Harmonic Distortion (THD) of the source current is also minimized, which suits the standard of International Electrotechnical Comission IEC 61000-3-2. By this methodology, the power factor of 0.99 is achieved with 97% efficiency and 3.92% THD. The proposed methodology is validated in simulation by MATLAB and in hardware by FPGA Spartan 6E.

This article introduces a switched reluctance motor speed control system based on fuzzy control, focusing on the improvement of the control performance of the speed control system by fuzzy control. This paper uses SIMULINK to simulate the speed control system. By introducing a fuzzy controller based on Mandani fuzzy control rules between the speed outer loop and the current inner loop of the built-up switched reluctance motor speed control system model, the speed fluctuation range of noload start is reduced from 3.2% to 2.3%o, the torque fluctuation range is reduced by 11.122%, and the current fluctuation range is greatly reduced. Compared with the speed control system under the traditional PI control, the control performance of the speed control system after the introduction of fuzzy control has been significantly improved.

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model’s nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC.

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.

This paper presents a control mechanism for speed control of switched reluctance motor (SRM) along with torque ripple minimization employing many optimizing liasion (MOL) technique. The control mechanism includes speed controller in the outer loop, current controller in the inner loop, intelligent selection of turn on and turn off angle for a 60kW, 3-phase 6/8 SRM. It is noticed that the torque ripple coefficient, ISE of speed & current are reduced by 10.43%, 36.53%, 16.01% respectively by MOL algorithm compared to gravitational search algorithm (GSA) algorithm. It is also observed that the settling times in torque, speed and current are reduced by 42%, 45.65% and 47.91% by proposed MOL algorithm compared to the GSA algorithm.

This paper presents a novel current mode control technique using an Adaptive Neuro Fuzzy Inference system (ANFIS) for single phase power factor correction (PFC) with DC- DC Single Ended Primary Inductance converter (SEPIC). The proposed system uses cascade control strategy which comprises of outer voltage PI controller and an inner current controller. The outer PI controller coefficients are determined by appropriate tuning and inner current control is implemented with AnFIS. The performance of the proposed system is analysed using Matlab/Simulink based simulation studies. In order to ensure the robustness of the proposed controller the performance parameters such as Percentage Total Harmonic Distortion(%THD), Power factor (PF),%Voltage regulation, %Efficiency are analyzed. From the simulation results, it is inferred that the proposed method provides efficient tracking of output voltage and source current for step load, set point and line variations.

The accurate rotor position information is very important for high performance operation of switched reluctance motor (SRM). Traditionally, there is a mechanical rotor position sensor. But this reduces the reliability and increases cost and size of SRM. In order to overcome the disadvantage of mechanical rotor position sensor, a sensorless operation method of SRM based on adaptive network based fuzzy inference system (ANFIS) is developed in this paper. The rotor position can be estimated by using the unique relationship between rotor position, flux linkage and phase current. In this paper, the ANFIS is used to map this relationship. Among the sensorless position estimation method, approach based on fuzzy neural network (FNN) is one of the promising methods. By combining the benefits of artificial neural network (ANN) and fuzzy inference system (FIS) in a single model, the ANFIS shows characteristics of fast and accurate learning, the ability of using both linguistic information and data information and good generalization capability. For its antecedents are fuzzy sets, the noise in the input signals can be restrained. This approach shows a characteristic of robustness. For its consequent is in linear function of input variables, it has a simple structure and low computation complexity. So, it is well suited to be applied on-line. Applying it to the rotor position estimation, a high accuracy and robust sensorless rotor position estimator is presented. The experimental results proved the effectiveness of the proposed method.