TR2023-022
Automated Controller Calibration by Kalman Filtering
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- "Automated Controller Calibration by Kalman Filtering", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2023.3254213, Vol. 31, No. 6, pp. 2350-2364, April 2023.BibTeX TR2023-022 PDF
- @article{Menner2023apr,
- author = {Menner, Marcel and Berntorp, Karl and Di Cairano, Stefano},
- title = {Automated Controller Calibration by Kalman Filtering},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2023,
- volume = 31,
- number = 6,
- pages = {2350--2364},
- month = apr,
- doi = {10.1109/TCST.2023.3254213},
- url = {https://www.merl.com/publications/TR2023-022}
- }
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- "Automated Controller Calibration by Kalman Filtering", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2023.3254213, Vol. 31, No. 6, pp. 2350-2364, April 2023.
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Abstract:
This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, filter coefficients, the sliding surface of a sliding mode controller, or weights of a neural network. Hence, the proposed method can be applied to a wide range of controllers. The method uses a Kalman filter that estimates control parameters, using data of closed-loop system operation. The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system. The performance-driven calibration method tunes the parameters online and robustly, is computationally efficient, has low data storage requirements, and is easy to implement making it appealing for many real-time applications. Simulation results show that the method is able to learn control parameters quickly, is able to tune the parameters to compensate for disturbances, and is robust to noise. A simulation study with the high-fidelity vehicle simulator CarSim shows that the method can calibrate controllers of a complex dynamical system online, which indicates its applicability to a real-world system. We also verify the real-time feasibility on an embedded platform with automotive- grade processors by implementing our method on a dSPACE MicroAutoBox-II rapid prototyping unit.