The Control System of Retarder Test Bench Based on Efficiency Optimization

In recent years, the speed-sensorless vector control of asynchronous motor has become a research focus in modern alternating current machine control. In order to further improve the performance of the speed-sensorless vector control of asynchronous motor, the speed estimation method of the speed-sensorless should have a high accuracy. In this dissertation, the estimation method of the model reference adaptive system is used to identify the actual speed of asynchronous motor and the voltage model and the current model are respectively used as the reference model and the adjustable model of the adaptive system, the self closed loop can be better achieved and the actual speed of asynchronous motor can be more accurately identified with the model reference adaptive system. Finally, speed-sensorless vector control system based on the model reference adaptive system is simulated and the actual speed of motor can be accurately detected. Therefore, this method has certain feasibility.

Better to meet the high performance AC speed adjustable speed control strategies and advanced control algorithms in the application of asynchronous motor, Introduced the hardware and software design of asynchronous motor control system development platform based on TMS320F2812. Discussed the vector control system for three-phase Asynchronous motor which use this platform as the control core, described the vector control principle and system structure, then simulate using SIMULINK, give the simulation results and analyse them to verify the feasibility of the design.

As one of the most important power equipment in industrial society, permanent magnet synchronous motor has a large number of uses in many fields. The traditional three-phase permanent magnet synchronous motor using vector control technology cannot meet the industrial high precision control requirements. So, the motor is analysed, the permanent magnet motor control model is constructed, and the chicken cluster algorithm is used to optimize the vector control parameters of the permanent magnet synchronous motor. Considering that the traditional cluster model faces the problem of local optimal solution, hypercube sampling and particle swarm model are used to optimize the chicken flock model, and an improved chicken flock motor control model is constructed to achieve efficient control of the motor. In the test of Rastrigin function, the particle swarm model, the traditional flock model and the improved flock model converge after 91, 72 and 20 iterations, respectively, and the best objective function values of 3.91e-4, 9.27e-5 and 1.79e-5 are obtained for the three models. The improved flock model has better convergence efficiency and convergence accuracy. In the test of different motor control methods, the overall fluctuation of the improved flock model is smaller, and the stability is improved by 31.65% compared with the traditional vector control, and 11.35% compared with the traditional flock model. The research content has important reference value for intelligent control of motors.

The vector control of LNG marine propulsion motor based on improved EKF is proposed for the problem of frequency operation of the controller which caused by the unknown external load disturbance that acting in the LNG marine propulsion motor system. The periodic torque ripples are reduced by introducing the speed error compensation based on improved EKF into the update algorithm of the vector control by utilizing the previous error data and error changing trend, and the results were compared with the traditional EKF vector control. It shows that the torque ripples of propulsion motor were minimized, the state observer accuracy and robustness performance of the LNG control system was improved.

In this paper, a efficiency optimization controller is studied for a vector controlled induction motor. The optimum flux-producing current is obtained utilizing radial basis function neural network (RBFNN). Comparing with the conventional neural network, the radial basis function neural network possesses characteristic of simple structure, fast convergence and strong generalization, it is suitable for real-time control. Considering the change of iron core loss resistance due to flux and frequency, a precise dynamic model of induction motor is built, and RBFNN is trained based on this model. When induction motor is in steady state, the trained RBFNN is used to optimize flux-producing current; while when induction motor is in the transient state, the flux-producing current recovers the rated value for ensuring the fast response. The simulation is done with Matlab/Simulink and the proposed method of control is realized adopting TMS320LF2407A. Simulation and experiment results show that the efficiency optimization is significant and speed response is rapid.

One of the challenging topics in inverse modeling is numerical efficiency and efficient optimization algorithms have been researched. In optimization algorithms, we need efficient perturbations to find a global minimum. Geostatistical methods combined with perturbation have an important role in generating input parameter fields. However, in cases where the given static sample data are not pertinent considering dynamic data, it is difficult to find a global minimum in the inverse modeling. To overcome this problem, we propose the concept of probabilistic sensitivity analysis. From the sensitivity analysis, cumulative probability distribution functions, which are used as indicators of perturbations, are constructed. Then, they are used for field generations in the gradual deformation method. By enforcing the restart of the optimization process at a local minimum state, the inverse modeling becomes stable and efficient. From the applications using various synthetic reservoirs, the proposed scheme works even in cases where reservoirs have hidden abnormal permeability values, which are not represented by sampling data.

In view of problems related to large loss, low efficiency, and poor performance of asynchronous motor in light load, the influence of rotor iron loss on motor operating efficiency is analyzed. A mathematical model of asynchronous motor vector control is established by considering iron loss, and a maximum efficiency control strategy based on optimal flux linkage is studied. Parameters, such as rotor flux and input power under different speeds and load torques, are tested under the condition of iron loss using Matlab/Simulink to build a system simulation platform. Results show that the system optimization scheme is feasible and effective.

The application of vector control to a conventional synchronous reluctance motor (VCSynRM) is presented with emphasis on the effects of saturation and iron losses. It is shown experimentally that these parasitic effects can significantly influence the performance of a VCSynRM. A simplified d-q model that includes saturation and iron losses is presented, and experimental results concerning optimal torque/ampere and optimal efficiency operation are shown to be in general agreement with the predictions of the model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In view of the operation characters of permanent magnet synchronous motor (PMSM) for hybrid electric vehicle (HEV) application, efficiency optimization control strategy is needed to meet the demand of rapidity. An efficiency optimized vector control scheme is presented in this paper. The initial value of d-axis current can be achieved in any constant speed and torque operating point by loss model, and optimized through the gradient search technique and the golden section technique. Results of experiments are provided to demonstrate the effectiveness of the proposed method.

본 논문은 동손과 철손을 최소화 하는 SynRM을 위하여 효율 최적화 알고리즘을 제시하였다. 속도 제어기의 설계는 퍼지제어와 신경회로망을 사용한 적응 퍼지-신경회로망(AFNN) 제어기에 기반을 두었다. 특정 토크는 d 와 q 축의 전류성분의 다양한 합성으로 얻을 수 있다. 효율최적화 제어는 정상상태에 일정한 동작점에서 최소 손실을 제공하는 d 와 q 축 전류 성분의 조합을 찾는다. 직접 조절되는 토크 발생은 효율최적화 제어 설계에 의하여 매우 잘 조절되어지는 전류 성분이다.<BR> 제시된 알고리즘은 속도변화와 양호한 토크제어를 위해 전자기적 손실을 허용한다. HAI 제어기의 제어성능은 다양한 동작상태 분석을 통하여 평가한다. 분석된 결과는 제시된 알고리즘의 타당성을 보여준다.

This paper deals with the performance improvement of the voltage-oriented vector control of induction motor which presents the merits of constant stator flux control, simple calculation and easy implementation. In this method, the d-axis of the synchronous reference frame is oriented in the direction of the stator voltage vector, and then a control strategy peculiar to the induction motor is obtained. Based on the previous work, much progress has been made to get better performance, including the enhanced system structure and a novel flux estimator. Simulation and experimental results show that by knowing only a few parameters of the induction motor such as Rs and J, the flux and torque can be controlled separately, and a simple but high-performance speed control can be achieved while avoiding the complex coordinate transformation in traditional vector control methods.

MotivationUnknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence.ResultsHere, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data.Availability and implementationThe proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.Supplementary information Supplementary data are available at Bioinformatics online.

In view of the operation characteristics of pump application, pump system should have high efficiency and energy saving during the whole operating process. A novel efficiency-optimization control strategy is presented to meet the demand of high efficiency of variable speed high efficiency PMSM. The core of this strategy is the excellent integration of mended maximum torque to current control algorithm, which is based on the losses model during the dynamic and the grade search method with changed step by fuzzy logic during the steady. A TMS320LF2407A DSP-based digital control inverter for variable speed high efficiency PMSM is developed. The performance experiments for control system of variable speed high efficiency PMSM have been completed. The tested result verified that the system can reliably operate with the different control strategy during the dynamic and steady operation, and the system has more perfect performances using the efficiency-optimization control.

In today's world, where technology is developing rapidly, electric drive systems play a key role in a huge range of applications: from industrial production lines to vehicles and household appliances. These systems require high efficiency, accuracy and reliability. It is here that there is a need to improve and optimize the processes of controlling electric drives.In recent decades, neural network controllers, or neuroregulators, have won their place in the world of automatic control. Their unique ability to model complex nonlinear dependencies makes them an ideal tool for applications in electric drive systems. Neuro-regulators can adapt to changing operating conditions, learn from data and optimize the control process depending on specific requirements and conditions.This work is devoted to the possibility of using a neuroregulator in the traction asynchronous electric drive system. In the Matlab/Simulink environment, a simulation model of the AD914-U traction asynchronous electric motor vector control system described in the d,q rotating coordinate system was developed. The synthesis of the NARMA-L2 neuroregulator was performed, which combines the principles of the autoregressive model and the moving average model to provide prediction and control of complex processes. The main idea of this controller is to build a nonlinear transformation of input data that can predict the future states of the system. To demonstrate the capabilities of neuroregulators in traction electric motor control systems, comparative modeling of the NARMA-L2 neuroregulator and the classical proportional-integral regulator was conducted.The results of simulation modeling show that the system with a neuro-regulator shows the best indicators of regulation of the given parameters in transient processes and is a promising tool in the development of high-performance and energy-efficient traction electric drives.

The paper analyze the basic principle of model reference adaptive system(MRAS) algorithm. In this paper, rotor position and speed estimation algorithm of permanent magnet synchronous motor was deduced based on MRAS, and it is applied to sensorless vector control system of permanent magnet synchronous motor. Sensorless vector control system based on MRAS is built under the Matlab/Simulink environment. The simulation experiment and result analysis for the situation of motor's starting, speed mutation and load mutation are done. Simulation results prove that the sensorless vector control system of permanent magnet synchronous motor based on the algorithm of MRAS takes good control performance, and the algorithm can accurately estimate the rotor position and speed of the motor.