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Nowadays, the dual closed-loop Proportional-Integral-Lead (PI-Lead) controller is widely used, especially for the servo systems where high performance is required for motion control, like lithography machines and vehicle-used Lidars. Considering complex industrial applications and working conditions, more comprehensive and systematic research about the structure and potential problem of the PI-Lead controller is necessary for enhancing its performance and robustness. In this paper, the failure mechanism of the classic Anti-Windup methods in the PI-Lead controller is analyzed. By adjusting the location of control blocks, a novel reversed-structure-based PI-Lead controller is proposed as a complement to the traditional anti-windup methods for effective operation, and to make the system stable under the severe impact disturbance. Then, a self-commissioning strategy is designed, based on a Fast Root Mean Square Error (FRMSE) index and without additional manual tuning factors. Compared with classic indicators, the proposed FRMSE index can achieve faster instability detection and accelerate the tuning process to protect the equipment.
Yangyang Chen; Ming Yang; KaiYuan Liu; Jiang Long; Dianguo Xu; Frede Blaabjerg. Reversed-Structure-Based PI-Lead Controller Anti-Windup Design and Self-Commissioning Strategy for Servo Drive Systems. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleYangyang Chen, Ming Yang, KaiYuan Liu, Jiang Long, Dianguo Xu, Frede Blaabjerg. Reversed-Structure-Based PI-Lead Controller Anti-Windup Design and Self-Commissioning Strategy for Servo Drive Systems. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleYangyang Chen; Ming Yang; KaiYuan Liu; Jiang Long; Dianguo Xu; Frede Blaabjerg. 2021. "Reversed-Structure-Based PI-Lead Controller Anti-Windup Design and Self-Commissioning Strategy for Servo Drive Systems." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
This paper presents a comprehensive study on a novel voltage injection based offline parameter identification method for surface mounted permanent magnet synchronous motors (SPMSMs). It gives solutions to obtain stator resistance, d- and q-axes inductances, and permanent magnet (PM) flux linkage that are totally independent of current and speed controllers, and it is able to track variations in q-axis inductance caused by magnetic saturation. With the proposed voltage amplitude selection strategies, a closed-loop-like current and speed control is achieved throughout the identification process. It provides a marked difference compared with the existing methods that are based on open-loop voltage injection and renders a more simplified and industry-friendly solution compared with methods that rely on controllers. Inverter nonlinearity effect compensation is not required because its voltage error is removed by enabling the motor to function at a designed routine. The proposed method is validated through two SPMSMs with different power rates. It shows that the required parameters can be accurately identified and the proportional-integral current controller auto-tuning is achieved only with very limited motor data such as rated current and number of pole pairs.
Jiang Long; Ming Yang; Yangyang Chen; Dianguo Xu; Frede Blaabjerg. A Novel Voltage Injection Based Offline Parameters Identification for Current Controller Auto Tuning in SPMSM Drives. Energies 2020, 13, 3010 .
AMA StyleJiang Long, Ming Yang, Yangyang Chen, Dianguo Xu, Frede Blaabjerg. A Novel Voltage Injection Based Offline Parameters Identification for Current Controller Auto Tuning in SPMSM Drives. Energies. 2020; 13 (11):3010.
Chicago/Turabian StyleJiang Long; Ming Yang; Yangyang Chen; Dianguo Xu; Frede Blaabjerg. 2020. "A Novel Voltage Injection Based Offline Parameters Identification for Current Controller Auto Tuning in SPMSM Drives." Energies 13, no. 11: 3010.
In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.
Ming Yang; Zirui Liu; Jiang Long; Wanying Qu; Dianguo Xu. An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares. Energies 2018, 11, 778 .
AMA StyleMing Yang, Zirui Liu, Jiang Long, Wanying Qu, Dianguo Xu. An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares. Energies. 2018; 11 (4):778.
Chicago/Turabian StyleMing Yang; Zirui Liu; Jiang Long; Wanying Qu; Dianguo Xu. 2018. "An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares." Energies 11, no. 4: 778.