scholarly journals A Sensorless Vector Control Using new BS_PCH Controller structureand SC MRAS Adaptive Speed Observer for Electric Vehicles

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Vector Control ◽  
Electric Vehicle ◽  
Measurement Errors ◽  
Fast Response ◽  
Vehicle Speed ◽  
Control Approach ◽  
Speed Regulation ◽  
Sensorless Vector Control ◽  
Simulation Results ◽  
Ev Model

In this paper, a new controll structure are proposed to sensorless vector control the in-wheel motordrive system of Electric Vehicle (EV) to improve its performance and robustness. The design of the controlleris based on Backstepping and Hamitolnion control combined with a improved stator current MRAS adaptivespeed observer proposed to estimate the vehicle speed and it also can compensate for the uncertainties causedby the machine parameter variations, measurement errors, and load disturbances, improving dynamicperformance and enhancing the robustness of the SPIM drive system, perfect tuning of the speed referencevalues, fast response of the motor current and torque, high accuracy of speed regulation. A global EV model isalso evaluated based on the vehicle dynamics in this paper. The simulation results lead to the conclusion thatthe proposed system for the propulsion system of electric vehicle is feasible. The simulation results on a testvehicle propelled by two SPIM showed that the proposed control approach operates satisfactorily

scholarly journals Discrete-time Sensorless Control Using new BS_SM Controller structure and VM_ SC MRAS Adaptive Speed Observer for The propulsion system of Ship

2021 ◽  
Vol 19 ◽  
pp. 257-267
Author(s):  
Ngoc Thuy Pham
Keyword(s):  
Measurement Errors ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Fast Response ◽  
Propulsion System ◽  
Neuron Network ◽  
Control Approach ◽  
Speed Regulation ◽  
Sensorless Vector Control ◽  
Simulation Results

In this paper, a (BS_SM) new Backstepping_ Sliding mode controll structure combined with a (VM_SC_ MRAS) improved stator current MRAS based on adaptive speed observer using neuron network and sliding mode are proposed to sensorless vector control for The propulsion system of Ship. The design of the controller is based on new BS and SM sructure to improve its performance and robustness. VM_SC_ MRAS improved adaptive speed observer is proposed to estimate the speed of propeller. The combination of BS-SM controller with VM_SC_MRAS adaptive speed observer can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and load disturbances, improving dynamic performance and enhancing the robustness of the SPIM drive system, perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation. The simulation results lead to the conclusion that the proposed system for the propulsion system of ship is feasible. The simulation results on a test ship propelled showed that the proposed control approach operates satisfactorily

scholarly journals Discrete-time Sensorless Control Using new BS_SM Controller structure and VM_ SC MRAS Adaptive Speed Observer for The propulsion system of Ship

2021 ◽  
Vol 20 ◽  
pp. 1-11
Author(s):  
Ngoc Thuy Pham
Keyword(s):  
Measurement Errors ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Fast Response ◽  
Propulsion System ◽  
Neuron Network ◽  
Control Approach ◽  
Speed Regulation ◽  
Sensorless Vector Control ◽  
Simulation Results

In this paper, a (BS_SM) new Backstepping_ Sliding mode controll structure combined with a (VM_SC_ MRAS) improved stator current MRAS based on adaptive speed observer using neuron network and sliding mode are proposed to sensorless vector control for The propulsion system of Ship. The design of the controller is based on new BS and SM sructure to improve its performance and robustness. VM_SC_ MRAS improved adaptive speed observer is proposed to estimate the speed of propeller. The combination of BS-SM controller with VM_SC_MRAS adaptive speed observer can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and load disturbances, improving dynamic performance and enhancing the robustness of the SPIM drive system, perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation. The simulation results lead to the conclusion that the proposed system for the propulsion system of ship is feasible. The simulation results on a test ship propelled showed that the proposed control approach operates satisfactorily.

Advanced Sensor-Less Control of IPMSM Motor Using Adaptive Neural FOC Approach

2019 ◽  
Vol 894 ◽  
pp. 149-157
Author(s):  
Pham Quoc Khanh ◽  
Ho Pham Huy Anh ◽  
Cao Van Kien
Keyword(s):  
Vector Control ◽  
Electric Vehicle ◽  
Permanent Magnet Synchronous Motor ◽  
The Novel ◽  
Electric Drives ◽  
Speed Regulation ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Speed Range ◽  
Simulation Results

This paper proposes a neural vector control (NN-FOC) for speed regulation of interior-mounted permanent magnet synchronous motor (IPMSM) drive. The weights of proposed neural NN-FOC structure are optimally identified based on the Levenberg-Marquardt algorithm. The novel MTPA approach is applied for IPMSM-based electric vehicle (EV) drive application. The novel neural NN-FOC control is verified in simulation tests and is compared with the traditional PI-FOC vector control. The simulation results prove that the maximum IPMSM speed range available with the new NN-FOC control is significantly improved in comparison with the traditional IPMSM PI-FOC control. As a consequent the proposed neural NN-FOC control can be successfully applied in advanced electric drives, particularly in PMSM-based electric vehicle EV drive application.

scholarly journals A study of sensorless vector control of IM using neural network luenberger observer

2020 ◽  
Vol 11 (3) ◽  
pp. 1259
Author(s):  
Tahar Belbekri ◽  
Bousmaha Bouchiba ◽  
Ismail khalil Bousserhane ◽  
Houcine Becheri
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vector Control ◽  
Induction Motor ◽  
Speed Control ◽  
Motor Simulation ◽  
Electronic Components ◽  
Luenberger Observer ◽  
Sensorless Vector Control ◽  
Simulation Results

After the development of electronic components, the elimination of the sensors has become a necessary subject to get good results in the field of speed control, because of the price of the sensors, the strenuous choice of its position and the disturbance of measurement which affects the robustness of control. The luenberger observer showed to be one of the most excellent methods suggested by the researchers; this is due to the best performance, it offers in terms of stability, reliability and less counting effort. In this article, a study of luenberger observer based on neural network-based was discussed. This artificial intelligence method makes it possible to decrease the error of estimated speed for IRFOC control of the induction motor. Simulation results are obtained to show the robustness and stability of the system.

Design of Sensorless Vector Control System for Induction Motors

2011 ◽  
Vol 58-60 ◽  
pp. 2046-2050 ◽  
Author(s):  
Jun Li Gao ◽  
Shi Jun Chen ◽  
Guo Cai Li
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
Orientation Angle ◽  
Online Identification ◽  
Speed Regulation ◽  
Sensorless Vector Control ◽  
Static Performance ◽  
Rotor Flux ◽  
Vector Control System ◽  
Model Reference Adaptive

Online identification of motor rotor speed by using modified rotor flux orientation angle estimator and model reference adaptive system achieves sensorless vector control of induction motor. The principle verification conducted on self-developed sensorless vector control of induction motor shows that the system has good dynamic & static performance and induction motor achieves significant improvement in speed regulation in the premise of not adding cost of general inverters.

Sensorless speed control of SPIM using BS_PCH novel control structure and NNSM_SC MRAS speed observer

2020 ◽  
Vol 39 (3) ◽  
pp. 2657-2677
Author(s):  
Ngoc Thuy Pham
Keyword(s):  
Measurement Errors ◽  
Control Structure ◽  
Sliding Mode ◽  
Current Model ◽  
Dynamic Performance ◽  
Speed Control ◽  
Fast Response ◽  
Speed Regulation ◽  
Speed Estimator ◽  
Model Reference Adaptive

This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer.

Research on the Driver's Following Behavior Based on Hybrid Electric Vehicle Model

2013 ◽  
Vol 281 ◽  
pp. 159-162
Author(s):  
Chang Hao Piao ◽  
Chong Xi Duan ◽  
Yu Sheng Li ◽  
Sheng Lu
Keyword(s):  
Electric Vehicle ◽  
Traffic Safety ◽  
Hybrid Electric Vehicle ◽  
Performance Testing ◽  
Good Effect ◽  
Behavior Model ◽  
Vehicle Speed ◽  
Vehicle Performance ◽  
Simulation Results ◽  
Hybrid Electric

On the basis of Hybrid Electric Vehicle (HEV) model, this paper has developed the driver's following behavior model. Reasonable following behavior is very important in traffic safety. Firstly, this paper has gotten the relationship between vehicle speed, acceleration and pedal depth by vehicle performance testing. Then car-following model based on regression analysis was developed, meanwhile reasonable amendments of the distance model was added. Finally，graphical user interface (GUI) of driver's following behavior was designed, which can displays the simulation results intuitively. Simulation results show that driver's following behavior model is reasonable and effective. It has a good effect both on traffic safety and traffic-economic.

scholarly journals A Traction Control Strategy with an Efficiency Model in a Distributed Driving Electric Vehicle

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Cheng Lin ◽  
Xingqun Cheng
Keyword(s):  
Sliding Mode Control ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Control Strategy ◽  
Sliding Mode ◽  
Emergency Situation ◽  
Vehicle Speed ◽  
Traction Control ◽  
Control Approach ◽  
Mode Control

Both active safety and fuel economy are important issues for vehicles. This paper focuses on a traction control strategy with an efficiency model in a distributed driving electric vehicle. In emergency situation, a sliding mode control algorithm was employed to achieve antislip control through keeping the wheels’ slip ratios below 20%. For general longitudinal driving cases, an efficiency model aiming at improving the fuel economy was built through an offline optimization stream within the two-dimensional design space composed of the acceleration pedal signal and the vehicle speed. The sliding mode control strategy for the joint roads and the efficiency model for the typical drive cycles were simulated. Simulation results show that the proposed driving control approach has the potential to apply to different road surfaces. It keeps the wheels’ slip ratios within the stable zone and improves the fuel economy on the premise of tracking the driver’s intention.

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