This article describes the characteristics of the observer.
The observer is essentially a state reconstruction, that is, reconstructing a system, using directly measurable variables in the original system as its input signal, and making the reconstructed state equivalent to the original system state under certain conditions. The principle of equivalence is that the errors of the two can approach zero asymptotically and stably during dynamic changes. This system used to achieve reconstruction is called an observer.
Observers are divided into deterministic observers and random observers according to signal types, and into linear observers and nonlinear observers according to the system. The basic structure of the observer is the state estimation equation formed by the mathematical model of the motor plus the correction link. The two constitute a closed-loop state estimation, that is, the observer. Scholars in the electrical field absorb theoretical results from many scientific fields in the world, combine the cutting-edge ideas of various disciplines and creatively integrate them into observer theory, forming many valuable observers with different control ideas. In pmsm sensorless technology, adaptive full-order observer, extended Kalman filter (ekf) and sliding mode observer (smo) are often used.
1. Adaptive full-order observer
Adaptive observer is a sensorless technology that merges adaptive control with observer theory. The basic idea is to introduce adaptive control into the correction link of the observer structure to achieve adaptive speed control. The pmsm adaptive full-order observer first constructs a current observer based on the voltage equation in the pmsm two-phase rotating coordinate system. Then use the standardized pmsm mathematical model as the reference model. Taking the constructed current observer as an adjustable model, two model output errors are used to drive the adaptive mechanism. Under the action of the adaptive law, the parameters to be estimated can be continuously modified so that the output error of the two models tends to zero. The adaptive observer can be used not only to estimate the position and speed of the pmsm rotor, but also to identify motor parameters based on the Popov stability theory, reducing the impact of parameter changes and improving the robustness of the system.
Kalman filter is also a kind of observer. It is the application of Kalman filtering idea in observer theory. The extended Kalman filter, like other observers, can track the state of the system. The difference is that it is nonlinear and random. The ekf state estimation is divided into two major stages: the prediction stage and the correction stage. In the forecast stage, the predicted value of the next estimate is calculated from the result of the previous estimate. In the correction stage, the actual output and the predicted output deviation are used for feedback correction of the predicted value. The essence of Kalman filter is to feedback and correct the predicted value. Therefore, it not only has optimization and adaptive capabilities, but also can better suppress measurement noise and system noise. However, the disadvantage of the ekf filter is the unknown of the system measurement noise and the system noise, which brings about the problem that it is difficult to select the covariance matrix in the ekf filter by a deterministic method. The covariance matrix is generally selected by trial and error, and the covariance matrix is related to the dynamic performance and stability of the system. Therefore, the determination of the covariance matrix is critical to the stability of the system.
3. Sliding mode observer
Sliding mode observer is an application of sliding mode variable structure control in observer theory. Its characteristic is that its performance is completely determined by its sliding mode hyperplane, no overshoot will occur during the transition process, and the entire system has strong robustness to its own parameter changes and external disturbances. The basic idea is to first establish a sliding mode current observer based on the pmsm mathematical model, and select the deviation between the observed current and the actual current of the sliding mode observer as the sliding mode hyperplane. The deviation is controlled by booming, and the induced electromotive force composition containing high harmonics is estimated. The system is closed loop, which contains high-order induced electromotive force and calculates the position and speed after filtering. It is the shortcoming of the sliding mode observer to estimate that the variable contains high-order harmonics, which affects the application in the high-performance servo system. Although filtering can be performed, the usual way of filtering will cause phase deviation. As mentioned earlier, the Kalman filter can consider the impact of noise on the system, and can effectively combine the sliding mode observer with the Kalman filter, and the components can take advantage of the Kalman filter to form a more complete observer.
This article is from HONG DA (HK) ELECTRONICS CO which offer electronic components, semiconductors, antennas, capacitors, connectors, diodes, transistors, IC,resistors. For more product information, please go to the website to get it.