Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer
Abstract
1. Introduction
- A new motorized valve structure driven by LATM is described in detail. We have fully considered the influence of motor rotor position and load current, magnetic field saturation and the cogging effect, improved the existing cogging structure LTAM model, and derived an accurate torque expression.
- Through accurate system dynamics modelling, simulation verification, and definition of the available angular range, the uncertainty in the original system is transformed into unknown but bounded uncertainty.
- The proposed approach presents an EM EGR valve servo system scheme with a dual closed-loop position and current, which achieves excellent positional accuracy and strong robustness to random shock-type disturbances through high-precision non-contact angle measurements, on-line load estimations, and feed-forward compensation.
2. Structure and Modeling
2.1. Introduction of Structure
2.2. Centralized Winding LTAM Modeling
2.3. Analytical Expression of the Electromagnetic Torque
3. Position Servo Control System Using a Kalman-Filter Based Load Observer
3.1. Position Servo System Structure
3.2. Kalman Filter Based Load Observer
- Calculate the a priori estimates of the state variables and the a priori estimates of the covariance matrix, as follows.
- Calculate the Kalman gain.
- The state estimation is corrected based on the measurements, and the optimal estimate of the state variable is calculated, also known as the a posteriori estimate, which is the optimized output of the algorithm.
- Calculate the posterior covariance matrix.
3.3. Simulation Verification
4. Position Servo System Implementation
5. Experimental Verification
5.1. Plant Dynamics
5.2. Positioning Accuracy
5.3. Verification of Shock-Type Disturbance Rejection
6. Conclusions
- (1)
- The servo system scheme proposed in this paper can compensate for the influence of external shock-type disturbances in real time through effective load observation and realize better valve positioning accuracy, dynamic response capabilities, and robustness.
- (2)
- The proposed Kalman filter-based load observation method can converge to the real load value within 15 ms and then realize unbiased estimations.
- (3)
- The proposed method showed a good real-time response and the ability to suppress external disturbances, and after the disappearance of the disturbance, the EM valve could be quickly restored to the reference position, with a maximum position offset of not more than 0.3 mm, a repeatable positioning error of not more than 0.002 mm after the disappearance of the disturbance, and a disturbance recovery time of not more than 250 ms. Additionally, consistent results were obtained in multiple repetitions of measurements, which demonstrates the validity and repeatability of this method.
- (4)
- The proposed method was not sensitive to the duration of the disturbance, and the values of position error, overshoot, disturbance recovery time, and other parameters caused by the disturbance of 0.5–1.2 s duration were almost at the same level.
- (5)
- Future work will be carried out in two directions: (1) Study of the influence of high temperatures on the dynamics of the plant under the actual application conditions, which could effectively improve the adaptability of the observer in different applications and further improve its observation accuracy; and (2) The failure mechanism of the observer and the constraints of the applied parameters, which could further improve the effectiveness of the application of the methods in this paper.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Symbol | Parameter | Value | Unit |
---|---|---|---|
R | Resistance of stator winding | 3.6 | Ω |
Equivalent inductance of winding | 32 | mH | |
J | Rotor moment of inertia | 0.002 | |
D | Viscous damping coefficient | 0.0024 | |
K | Torsion spring stiffness | 0.12 | |
Electromagnetic torque coefficient | 0.126 | ||
Back electromagnetic force coefficient | 0.126 |
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Yang, Y.; Cheng, X.; Zhou, R. Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer. Energies 2024, 17, 4515. https://doi.org/10.3390/en17174515
Yang Y, Cheng X, Zhou R. Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer. Energies. 2024; 17(17):4515. https://doi.org/10.3390/en17174515
Chicago/Turabian StyleYang, Yi, Xin Cheng, and Rougang Zhou. 2024. "Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer" Energies 17, no. 17: 4515. https://doi.org/10.3390/en17174515
APA StyleYang, Y., Cheng, X., & Zhou, R. (2024). Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer. Energies, 17(17), 4515. https://doi.org/10.3390/en17174515