Yanru Zhong

Research on Robust Performance of Speed-Sensorless Vector Control for the Induction Motor Using an Interfacing Multiple-Model Extended Kalman Filter

The interfacing multiple-model extended Kalman filter (IMM-EKF) is proposed here as a modification of the extended Kalman filter (EKF). In this algorithm, two multiple-model EKF groups are built, one group is the optimum model, and the other is the noise model. Each model group is created by multiple models, and it will get good performance at stable state and robust ability when disturbance occurred. The algorithm gets the estimation value by mixing the outputs of the different model in different weightings, and the calculation of weightings is researched. Whether the IMM-EKF can give better estimation performances and robust ability than the EKF for speed estimation of induction machines is explored in this paper. Via simulations and experiments, estimated error and the change of flux linkage by disturbance based on the IMM-EKF and EKF is compared. The simulation results show that the IMM-EKF has the better estimation performance of antigross error than the EKF.

Research on Anti-Error Performance of Speed and Flux Estimator for Induction Motor Using Robust Reduced-Order EKF

A novel speed and flux estimator of induction motor based on robust reduced-order extended Kalman filter (RROEKF) is proposed in this paper, and the effect of gross error on estimation accuracy of reduced-order EKF (ROEKF) was analyzed. Whether RROEKF can give better estimation performance and robust ability than ROEKF for speed and flux estimation of induction machines is explored. The speed estimated error and the flux fluctuation of RROEKF by gross external disturbance and internal estimated error is compared with ROEKF. The simulation and experimental results show that RROEKF has the better estimation performance of anti-gross error than ROEKF and EKF.

Study and Control of Three-Phase PWM Rectifier Based on Dual Single-Input Single-Output Model

A novel dual single-input single-output (DSISO) model for a three-phase two-level PWM rectifier is proposed, in order to simplify the complicated structure of multiple input variables and multiple control objectives. In the proposed model, the three-phase PWM rectifier is equivalent to two single-phase PWM rectifiers in the α-β stationary reference frame, which is easier to analyze than a three-phase PWM rectifier. Based on the DSISO model, the small-signal model is derived, the control-to-output transfer function is deduced, and the design principle of the voltage controller is given. The control strategy is analyzed to determine switching control signals in a switching period. Experimental results confirm the validity of the DSISO model and the feasibility of the control strategy based on DSISO model.

A Speed and Flux Observer of Induction Motor Based on Extended Kalman Filter and Markov Chain

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.

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

On-line identification methods of parameters for permanent magnet synchronous motors based on cascade MRAS

Motor parameters should be on-line estimated to realize precise control of PMSM in sensorless vector control system. In this paper, an on-line identification method for PMSM parameters based on cascade MRAS is proposed by analyzing the conventional MRAS. By means of Popovs hyper-stability theory, the model of motor parameters identification is built in synchronous d-q coordinates, and PMSM stator voltage, stator current and their errors are used to obtain the adaptive laws of motor parameters, and it is realizable to estimate rotor speed, stator resistance and rotor flux at the same time. The simulation results demonstrate the correctness and effectiveness of the proposed method.

Stability and dynamic performance improvement of adaptive full-order observers in sensorless induction motor drives

To assure the stability and dynamic performance of the sensorless induction motor drives, the design strategy of both feedback gains and adaptation gains for an adaptive full-order observer (AFO) is a necessary issue. In this paper, to accomplish both requirements, a simplified design of feedback gains of AFO is studied, and the selection method of the optimal value for feedback gains is discussed using the method of Popov hyper stability theory. For the adaptation proportional-integral (PI) gains, a novel optimization method of the adaptation PI gains is proposed to enhance the dynamic performance of the speed estimation system. With the proposed variable arguments PI, the speed estimation achieves higher adaptability. The validity of the proposed method is verified by simulation results.

A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter

A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter (STEKF) is proposed in this paper, implementing optimization of speed sensorless vector control. With this method, the fading factor is introduced into the covariance matrix of the predicted state, which forces the residual sequences orthogonal to each other and tunes the gain matrix online. The estimation error is adjusted adaptively, and the mutational state is tracked fast. The proposed method shows more robust against the model uncertainties or the time-varying parameter systems, and it has better tracking ability to the mutations and the slow changes. 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.

Research on stability performance of speed estimation for induction motors based on adaptive full-order observer

To assure the stability performance of the speed estimation in the speed sensorless induction motor drives, the design strategy of feedback gain matrix for an adaptive full-order observer is a necessary issue. In this paper, the major cause of instability phenomenon based on adaptive full-order observer is analyzed, and a novel design of observers feedback gain matrix is proposed to achieve the stability over the whole operating range especially in the low speed region, including the regenerating mode. Besides, the Popov hyper-stability theory is used to prove the stability improvement based on the proposed method. Some simulation results are given to demonstrate the effectiveness of the proposed solution.

A speed and flux estimation method of induction motor using fuzzy extended kalman filter

A speed and flux estimation method of induction motors using fuzzy extended kalman filter(FEKF) is proposed in this paper, which is used to make lower impact of time varied statistic of measurement noise. It reaches a better speed estimation accuracy of induction motors than the extended kalman filter(EKF). The proposed algorithm modifies the measurement noise covariance of extended kalman filter recursively by monitoring if the ratio between filters innovation and actual innovation is near 1, and chooses a fuzzy factor to make its noise model close to real noise model adaptively. The speed estimated error and the flux fluctuation of FEKF under gross external disturbance and unknown measurement noises are compared with EKF. Simulation and experimental results show that FEKF provides better performance and faster convergence than EKF under gross external error and unknown measurement noises.