此无模型控制方法非常适用于实际的模型参数难以辨识，且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose model parameter are very difficult to be identified and time varying.
本文介绍了一种可互换模型参考自适应方法实现对电机转速和定子电阻的辨识，并将其应用于异步机的无速度传感器直接转矩控制中。
A mutual model reference adaptive method for the rotor speed and stator resistance identification is proposed in this paper to implement a speed sensorless direct torque control of induction motors.
无模型控制方法非常适用于实际的阶数难以知道或难以辨识，且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose orders and modeling are very difficult to be known and time varying.
根据异步电动机直接转矩控制原理，提出了采用人工神经元网络速度辨识方法去实现无速度传感器的交流调速控制系统。
Based on the principle of the direct torque control, the novel identification method of induction motor speed using neural network in a speed-less sensor AC drive system was presented.
为了提高系统的跟踪准确度，笔者提出在线辨识参数的改进无差拍控制策略。
In order to improve the track accuracy of the system, the on-line identification parameter method is used and the parameters of controller can be corrected instantly.
通过李亚普诺夫稳定性理论，推导出一种无速度传感器控制的速度自适应辨识算法。
The adaptive speed recognition algorithm without speed sensor control was deduced by applying the lyapunov stability theory.
结果表明，在无噪和有噪情况下，神经网络模型的辨识精度和泛化能力都要优于传统方法。
Compared with the classical method, the identification accuracy and the generalization capability of nn are testified to be superior in either the free - noise or noisy case.
速度辨识是无速度传感器直接转矩控制的核心问题。
The speed estimation is core question of the speed sensorless direct torque control system.
受外部干扰和模型误差影响，异步电机无速度传感器矢量控制中的转速辨识结果往往不准确。
Traditional speed identification methods for sensorless vector control of induction motors are inaccurate due to external disturbances and model errors.
理论分析和仿真结果都表明估计结果具有渐近无偏性和一致收敛性，该方法辨识精度高，具有良好的实用性。
The theory analysis and simulation results show that the estimation is asymptotically unbiased and has strong consistency, and that the new method is very efficient and practical.
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本文针对无速度传感器异步电机矢量控制系统，提出了一个基于卡尔曼滤波器的异步电机速度辨识方法。
For a vector control of a speed sensor less induction motor drive system, a novel speed estimated method using an extended Kalman filter is proposed.
本文讨论含有多重积分环节的无自平衡系统的辨识问题。
This paper deals with the identification of nonself-balanced system with multiple integral link.
仿真结果表明，所提出的无网侧电动势传感器控制技术具有良好的辨识速度，且辨识精度高，系统鲁棒性强。
Simulation results show that the designed source voltage sensorless control technology has a good recognition rate, and high identification precision, a strong robustness.
对零低速区域速度的有效辨识是异步电动机无速度传感器控制的一个重要课题。
Effective speed identification in low speed and standstill area is an important item for speed sensorless control of asynchronous motor.
本文基于辨识 ARMA新息模型生成估计残差序列 ，再对残差序列的平均值和无偏方差进行假设检验 ，可实现工序质量的异常诊断。
In this paper, a method of process quality diagnosis using hypothesis testing for residual sequence of ARMA innovation model estimation error by recursive maximum likelihood method was studied.
基于全阶观测器，采用李亚普诺夫稳定性理论对电机转速进行在线辨识，实现了异步电机无速度传感器直接转矩控制系统。
Motor speed was estimated on line base on Lyapunov's stability theory, and in this the speed sensorless direct torque control emulation system was designed.
根据感应电机无速度传感器矢量控制原理，采用遗传算法进行转速辨识。
Speed estimator of sensorless vector control system based on variable argument PI adaptive mechanism;
根据感应电机无速度传感器矢量控制原理，采用遗传算法进行转速辨识。
Speed estimator of sensorless vector control system based on variable argument PI adaptive mechanism;
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