A Method to Improve the Response of a Speed Loop by Using a Reduced-Order Extended Kalman Filter

Abstract

In servo systems, encoders are usually used to measure the position and speed signals of electric machines. But in a low speed range, the traditional M/T method has a larger time delay, which will cause an increase of the speed loop order and degradation of the speed loop performance. A method employed to reduce the delay of speed feedback by using a reduced-order Extended Kalman Filter (EKF) is introduced in this paper. The speed of the permanent magnet synchronous motor is estimated by the reduced-order EKF in a low speed range, which reduces the delay of speed feedback and extends the cutoff frequency of the speed loop to improve the dynamic performance of the servo system. In order to solve the issues that the traditional full-order EKF is sensitive to the inertia of the system and computationally complex, a composite load torque observer (CLTO) is proposed in this paper. The load torque and the friction torque are simultaneously observed by the CLTO. Additionally, the CLTO is used to reduce the order of the EKF, which reduces the sensitivity of EKF on inertia to enhance the robustness of the algorithm and simplifies the computational complexity. The feasibility and effectivity of the above method are verified by simulations and experiments.

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

“…Thus, the behavior of the PMSM control system in which the controller is LADRCtype and the external disturbance-type signal is estimated using an ESO-type observer is equivalent to an ideal system (20) and (21). Note that Equation ( 21) is of ideal double integrator, and the command signal u 0 can be chosen in the following form:…”

“…Also, by using state estimators and, in particular, by estimating the PMSM rotor speed, sensorless control systems are obtained that have the advantage of providing high system reliability by eliminating additional hardware structures such as speed sensors. Common state estimators include the Luenberger Observer [14,15], Model Reference Adaptive System (MRAS) [16,17], and Sliding Mode Observer (SMO) [18,19], for the case where the deterministic approach is chosen, and for the case where the stochastic approach is chosen, estimators of the Kalman filter type are used [20,21].…”

Review on Embedded Methods in Parameter Estimation and Auto-tuning of Permanent Magnet Synchronous Servo Machine Driven Industrial Systems

“…While reduced order observers are used to calculate these fundamental mechanical parameters , , (or ) [45], [46], generally, conventional observers work with an estimator or predictor block (i.e. Kalman filter) [47]. Also, adaptive, sliding-mode, disturbance observers are the basic embeddable techniques for parameter estimation and tuning process [48], [49].…”

An enhanced sensorless control based on active disturbance rejection controller for a PMSM system: design and hardware implementation

“…In Hashlamon (2020), an extended kalman filter (EKF) is proposed to handle the uncertainties. However, the disadvantages such as computational complexity and parameter sensitivity of the EKF have limited its application to practical engineering (Liu et al , 2018). An infinite impulse response (IIR) filter is another alternative filter, which can attenuate the high frequency noise to satisfy the requirements of the signal acquisition.…”