What is the role of the research on torque and peak current optimization of switched reluctance motor based on improved torque distribution function for modern technology?
With the wide application of motors in the field of industry and transportation, improving the torque control performance of motors and reducing torque ripple has become one of the research hotspots. Through the in-depth study of the torque and current characteristics of the motor, it aims to improve the working efficiency, stability and reliability of the motor. This article will introduce the background of the research, its significance, and what will be explored.
One. Improved method of torque distribution function
The torque distribution function plays a key role in the torque control of switched reluctance motors. By reasonably distributing the phase current and excitation current of the motor, the torque distribution function can affect the torque output and performance of the motor.
However, the traditional torque distribution function has some limitations and cannot accurately predict and control the torque of the motor. To overcome these problems, researchers have proposed a number of improved torque distribution functions. I will introduce several common methods of improving the torque distribution function and analyze their advantages and scope of application.
Traditional torque distribution functions are often designed based on empirical formulas, but this method cannot adapt to different operating conditions and changes in motor parameters. To improve this problem, the researchers proposed an improvement method based on an empirical formula.
This method establishes a more accurate empirical formula to describe the relationship between torque and current by testing and experimenting with motors under multiple operating conditions. At the same time, mathematical methods such as curve fitting and regression analysis can be used to optimize the parameters of the formula to improve the accuracy and precision of torque prediction.
Another way to improve the torque distribution function is based on mathematical models. By establishing the mathematical model of the motor, considering the electromagnetic characteristics, current distribution and magnetic field distribution of the motor, a more accurate torque distribution function can be obtained.
This method requires modeling and parameter identification of various parameters of the motor, such as resistance, inductance, magnetoresistance, etc. At the same time, the nonlinear characteristics and dynamic response of the motor need to be considered to ensure the accuracy and reliability of the model.
With the rapid development of artificial intelligence technology, the improved method based on artificial intelligence has been widely used in the research of torque distribution function. For example, artificial intelligence techniques such as neural networks, fuzzy logic systems, genetic algorithms, etc. can be used to optimize the torque distribution function.
These methods can build a torque model of the motor through learning and training, and make torque distribution based on real-time input data. Compared with traditional methods, the improved method based on artificial intelligence can better adapt to the nonlinear characteristics and complex working conditions of the motor.
By improving the design and optimization method of the torque distribution function, the torque control performance and accuracy of the switched reluctance motor can be improved. The improved method based on empirical formula can optimize the parameters of the torque distribution function through experiments and curve fitting techniques to improve the accuracy of torque prediction. The improved method based on the mathematical model can establish a more accurate motor model, taking into account the electromagnetic characteristics and nonlinear factors of the motor to improve the accuracy and reliability of torque distribution.
One. Optimize the torque control strategy
In switched reluctance motor applications, torque control is a critical part. By optimizing the torque control strategy, the torque output of the motor can be accurately controlled and the performance and efficiency of the system can be improved. This article will discuss several common optimization torque control strategies, including traditional PID control, model predictive control, adaptive control, and advanced optimization algorithms.
1. Traditional PID control strategy:
PID control is a commonly used torque control strategy, which adjusts the control signal of the motor to achieve accurate control of torque by proportional, integral and differential operations on the error signal of the motor. The PID control strategy is simple and practical and suitable for many torque control applications. However, traditional PID control methods have limitations when dealing with nonlinear and time-varying systems, require empirical adjustment parameters, and may not be able to meet the precise torque control requirements for complex motor systems.
2. Model predictive control strategy:
Model Predictive Control (MPC) is a control strategy based on the mathematical model of the system for prediction and optimization. By building a mathematical model of the motor and making predictions within each sampling cycle, MPC is able to optimize the control signal of the motor to achieve accurate torque control.
Compared to traditional PID control, MPC can handle nonlinear and time-varying systems better, with greater robustness and performance. However, the computational complexity of MPC is high and it is necessary to solve optimization problems in real time, so the limitations of computing resources and real-time need to be considered in practical applications.
3. Adaptive control strategy:
Adaptive control is a control strategy that adjusts control parameters according to real-time changes in the motor system. By introducing an adaptive mechanism, the control parameters of the motor can be automatically adjusted according to the changes of system status and parameters to adapt to the torque control needs under different working conditions.
Adaptive control can improve the adaptability and robustness of the system, and can cope with the influence of factors such as motor parameter changes and load fluctuations. However, the design and parameter adjustment of adaptive control are more complex, and the stability and convergence of the system need to be fully considered.
Optimizing the torque control strategy is an important task in switched reluctance motor applications. Traditional PID control strategies are simple and practical, but may have limitations for complex nonlinear and time-varying systems. By establishing mathematical models and optimization methods, the model predictive control strategy can achieve more accurate torque control, but the calculation complexity is high. The adaptive control strategy can adjust the parameters according to real-time changes to improve the adaptability and robustness of the system, but the design and parameter adjustment are more complicated.
