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

Yin, Zhonggang,Li, Guoyin,Du, Chao,Sun, Xiangdong,Liu, Jing,Zhong, Yanru The Korean Institute of Power Electronics 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.1

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.

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

Zhonggang Yin,Guoyin Li,Chao Du,Xiangdong Sun,Jing Liu,Yanru Zhong 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.1

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.

Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Zhonggang Yin,Lei Gong,Chao Du,Jing Liu,Yanru Zhong 전력전자학회 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Yin, Zhonggang,Gong, Lei,Du, Chao,Liu, Jing,Zhong, Yanru The Korean Institute of Power Electronics 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

On-line Identification Methods of Parameters for Permanent Magnet Synchronous Motors Based on Cascade MRAS

Yanqing Zhang,Zhonggang Yin,Xiangdong Sun,Yanru Zhong 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6

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 Popov’s 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.

Design and Implementation of an Adaptive Sliding-Mode Observer for Sensorless Vector Controlled Induction Machine Drives

Yanqing Zhang,Zhonggang Yin,Jing Liu,Xiangqian Tong 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.3

An adaptive sliding-mode observer for speed estimation in sensorless vector controlled induction machine drives is proposed in this paper to balance the dilemma between the requirement of fast reaching transient and the chattering phenomenon reduction on the sliding-mode surface. It is well known that the sliding-mode observer (SMO) suffers from the chattering phenomenon. However, the reduction of the chattering phenomenon will lead to a slow transient process. In order to balance this dilemma, an adaptive exponential reaching law is introduced into SMO by optimizing the reaching way to the sliding-mode surface. The adaptive exponential reaching law is based on the options of an exponential term that adapts to the variations of the sliding-mode surface and system states. Moreover, the proposed sliding-mode observer considering adaptive exponential reaching law, which is called adaptive sliding-mode observer (ASMO), is capable for reducing the chattering phenomenon and decreasing the reaching time simultaneously. The stability analysis for ASMO is achieved based on Lyapunov stability theory. Simulation and experimental results both demonstrate the correctness and the effectiveness of the proposed method.

A Novel Speed Estimation Method of Induction Motors Using Real-Time Adaptive Extended Kalman Filter

Yanqing Zhang,Zhonggang Yin,Guoyin Li,Jing Liu,Xiangqian Tong 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.1

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi–Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

Internal model control of induction motors based on extended state observer

Liu, Jing,Yin, Zhonggang,Bai, Cong,Du, Na The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.1

The conventional internal model control (IMC) has been used widely due to its advantages of less computational burden and simple implementation. Since the internal model controller has a fixed filter, disturbances created by mismatched models, parameter variations and other unstructured dynamic uncertainties in induction motors (IMs) cannot be eliminated by a fixed IMC. To solve these problems, the control strategy of an induction motor using internal model control with an extended state observer (IMC-ESO) was proposed. IM parameter variations and other unstructured dynamic uncertainties are considered in IM drives. Based on this model, an ESO is defined as a hypothetical equivocal function. Then the estimated disturbance is applied as a feed-forward compensation to accurately control the current loop. Since the designed ESO works concurrently with IMC, the fast dynamic response of the IMC is maintained. The feasibility and validity of the proposed method are validated by experimental results.

A Novel Speed Estimation Method of Induction Motors Using Real-Time Adaptive Extended Kalman Filter

Zhang, Yanqing,Yin, Zhonggang,Li, Guoyin,Liu, Jing,Tong, Xiangqian The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.1

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

Design and Implementation of an Adaptive Sliding-Mode Observer for Sensorless Vector Controlled Induction Machine Drives

Zhang, Yanqing,Yin, Zhonggang,Liu, Jing,Tong, Xiangqian The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.3

An adaptive sliding-mode observer for speed estimation in sensorless vector controlled induction machine drives is proposed in this paper to balance the dilemma between the requirement of fast reaching transient and the chattering phenomenon reduction on the sliding-mode surface. It is well known that the sliding-mode observer (SMO) suffers from the chattering phenomenon. However, the reduction of the chattering phenomenon will lead to a slow transient process. In order to balance this dilemma, an adaptive exponential reaching law is introduced into SMO by optimizing the reaching way to the sliding-mode surface. The adaptive exponential reaching law is based on the options of an exponential term that adapts to the variations of the sliding-mode surface and system states. Moreover, the proposed sliding-mode observer considering adaptive exponential reaching law, which is called adaptive sliding-mode observer (ASMO), is capable for reducing the chattering phenomenon and decreasing the reaching time simultaneously. The stability analysis for ASMO is achieved based on Lyapunov stability theory. Simulation and experimental results both demonstrate the correctness and the effectiveness of the proposed method.