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A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive. [ABSTRACT FROM AUTHOR]

To improve the stability and accuracy of the estimated position using the conventional sliding mode observer (CSMO) under low frequency ratio (LFR), this article proposes a novel discrete-time synchronous-frequency adaptive observer (SFAO) for surface-mounted permanent magnet synchronous motor (SPMSM) sensorless drives. The proposed SFAO has merits of high amplitude and zero-phase lag of resonant controller at synchronous frequency. The sign function and the low-pass filter (LPF) of CSMO in the proposed observer can be removed and conducive to dispel the inherent chattering of the CSMO as well as phase shift produced by the LPF. Then, taking into account the digital control delay and discretization truncation error, an exact complex-vector discrete-time model in the stationary coordinate is developed. Based on that, the direct discrete-time domain design approach is utilized in the proposed observer, which can be devised by pole placement directly on complex z-domain to assure the stability and position estimation accuracy under LFR. Finally, the availability of the proposed discrete-time SFAO scheme is verified via experiments on a 2.1-kW SPMSM drive. [ABSTRACT FROM AUTHOR]

Due to various nonideal factors, including the motor parameter mismatches, detection errors, converter nonlinearities, noise, etc., dc bias and high-order harmonics exist in the flux model, which make the traditional flux observer estimation inaccurate. In order to suppress dc bias and high-order harmonics, an interior permanent magnet synchronous motors (IPMSM) sensorless drive method based on a fifth-order flux observer (FOFO) is proposed in this article. The proposed FOFO can completely remove dc bias and has a strong filtering ability for high-order harmonics. Additionally, the parameters of the FOFO are set through the s-domain analysis. Then, the discrete FOFO is obtained for better implementation in digital systems. The proposed FOFO is verified by experiments on a 2.0-kW IPMSM drive platform. [ABSTRACT FROM AUTHOR]

A traditional linear extended state observer (LESO) does not have enough capacity to handle the rapidly-varying back electromotive force (EMF). This key weakness limits the application of LESO in the internal permanent magnet synchronous motor (IPMSM) sensorless control system. In this article, an IPMSM sensorless drive method using an improved LESO (ILESO) is proposed. The ILESO consists of a pure integral and rapidly-varying sinusoidal interference estimator. The rapidly-varying sinusoidal interference estimator is added to the unknown interference estimation loop to estimate rapidly-varying back EMF. And the frequency of the rapidly-varying sinusoidal interference estimator changes adaptively with the operating frequency of the motor to guarantee the desired estimation accuracy of back EMF in a wide-speed range. This design makes it possible to observe the back EMF in a relatively low bandwidth, thereby solving the tradeoff between LESO bandwidth and high-frequency noise filtering. Then, an enhanced phase-locked loop is presented to improve the ability of tracking the rapidly-changing speed. The effectiveness of the proposed method is verified at a 2.0-kW IPMSM sensorless drive. [ABSTRACT FROM AUTHOR]

A novel sliding mode observer (SMO) with optimized constant rate reaching law for speed estimation in sensorless induction motor drives is proposed in this article to balance the dilemma between the requirement of fast reaching transient and the chattering reduction on sliding mode surface. By considering the reaching way to the sliding mode surface, a widely used reaching law called constant rate reaching law (CRRL) is introduced into SMO. However, SMO with CRRL cannot satisfy the rapidity of transient performance and the low chattering level simultaneously. To solve this problem, an optimized constant rate reaching law (OCRRL), which is based on the options of an exponential term that adapts to the variations of sliding mode surface and system states, is introduced into SMO. The proposed SMO with OCRRL is able to decrease the reaching time and suppress the chattering problem simultaneously. Simulation and experimental results both show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time speed identification method by using a symmetric strong tracking extended Kalman filter (SSTEKF) for induction motor sensorless drive. In SSTEKF, the residual sequences are forced orthogonal to each other, and the gain matrix is tuned in real-time by introducing fading factors into the covariance matrix of the predicted state. The modeling error is reduced, and the mutational state is tracked rapidly based on SSTEKF. Simultaneously, the Cholesky triangular decomposition is used to change the working way of the multiple fading factor matrix in the error covariance matrix. The application of the Cholesky triangular decomposition guarantees that the error covariance matrix is symmetric in the process of iteration, and the stability of the algorithm is enhanced. Therefore, the estimation accuracy, the tracking speed, and the noise suppression of the proposed method are better than the EKF. The correctness and effectiveness of the proposed method are verified by experimental results. [ABSTRACT FROM AUTHOR]

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed and flux estimation method based on the multiple-model extended Kalman filter (EKF) with Markov chain for IMs is proposed in this paper. In this algorithm, the multiple model EKF for speed and flux estimation is established, and the transition of the models obeys the Markov chain and the estimation value is obtained by mixing the outputs of different models in different weightings, and the calculation of the weighting is researched. Simultaneously, the transition probability can be continuously self-tuned by the residual sequence, the prior information is modified by the posterior information, and the more accurate transition among the models is obtained. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results. [ABSTRACT FROM PUBLISHER]

A speed and flux estimation method for induction motors (IMs) based on robust adaptive state observer is proposed in this paper as a modification of the adaptive state observer. The effect of gross errors on estimation accuracy of the adaptive state observer is analyzed. Whether the robust adaptive state observer can give better estimation performance and robust ability than traditional adaptive state observer for speed and flux estimation of IMs is discussed. Through the simulation and experiments, the speed estimated error and the flux fluctuation of robust adaptive state observer by gross external disturbance and internal estimated error is compared with traditional adaptive state observer. The simulation and experimental results show that the robust adaptive state observer has the better estimation performance of antigross error than traditional adaptive state observer. [ABSTRACT FROM PUBLISHER]