Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Run-to-Run Control With Bayesian Optimization for Soft Landing of Short-Stroke Reluctance Actuators (2401.13606v1)

Published 24 Jan 2024 in eess.SY and cs.SY

Abstract: There is great interest in minimizing the impact forces of reluctance actuators during commutations, in order to reduce contact bouncing, acoustic noise and mechanical wear. In this regard, a run-to-run control algorithm is proposed to decrease the contact velocity, by exploiting the repetitive operations of these devices. The complete control is presented, with special focus on the optimization method and the input definition. The search method is based on Bayesian optimization, and several additions are introduced for its application in run-to-run control, e.g. the removal of stored points and the definition of a new acquisition function. Additionally, methods for the input parametrization and dimension reduction are presented. For analysis, Monte Carlo simulations are performed using a dynamic model of a commercial solenoid valve, comparing the proposed search method with two alternatives. Furthermore, the control strategy is validated through experimental testing, using several devices from the same ensemble of solenoid valves.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (20)
  1. P. Eyabi and G. Washington, “Modeling and sensorless control of an electromagnetic valve actuator,” Mechatronics, vol. 16, no. 3-4, pp. 159–175, apr 2006.
  2. P. Mercorelli, “An Antisaturating Adaptive Preaction and a Slide Surface to Achieve Soft Landing Control for Electromagnetic Actuators,” IEEE/ASME Trans. Mechatronics, vol. 17, no. 1, pp. 76–85, feb 2012.
  3. M. Rahman, N. Cheung, and Khiang Wee Lim, “Position estimation in solenoid actuators,” IEEE Trans. Ind. Appl., vol. 32, no. 3, pp. 552–559, 1996.
  4. E. Ramirez-Laboreo, E. Moya-Lasheras, and C. Sagues, “Real-Time Electromagnetic Estimation for Reluctance Actuators,” IEEE Trans. Ind. Electron., vol. 66, no. 3, pp. 1952–1961, mar 2019.
  5. Y. Wang, F. Gao, and F. J. Doyle, “Survey on iterative learning control, repetitive control, and run-to-run control,” J. Process Control, vol. 19, no. 10, pp. 1589–1600, dec 2009.
  6. E. Ramirez-Laboreo, C. Sagues, and S. Llorente, “A New Run-to-Run Approach for Reducing Contact Bounce in Electromagnetic Switches,” IEEE Trans. Ind. Electron., vol. 64, no. 1, pp. 535–543, 2017.
  7. B. Srinivasan, D. Bonvin, E. Visser, and S. Palanki, “Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty,” Comput. Chem. Eng., vol. 27, no. 1, pp. 27–44, jan 2003.
  8. L. M. Rios and N. V. Sahinidis, “Derivative-free optimization: a review of algorithms and comparison of software implementations,” J. Glob. Optim., vol. 56, no. 3, pp. 1247–1293, jul 2013.
  9. H. Abdelrahman, F. Berkenkamp, J. Poland, and A. Krause, “Bayesian optimization for maximum power point tracking in photovoltaic power plants,” in 2016 Eur. Control Conf., 2016, pp. 2078–2083.
  10. A. Baheri, S. Bin-Karim, A. Bafandeh, and C. Vermillion, “Real-time control using Bayesian optimization: A case study in airborne wind energy systems,” Control Eng. Pract., vol. 69, pp. 131–140, dec 2017.
  11. E. Moya-Lasheras, E. Ramirez-Laboreo, and C. Sagues, “A Novel Algorithm Based on Bayesian Optimization for Run-to-Run Control of Short-Stroke Reluctance Actuators,” in 2019 Eur. Control Conf., jun 2019, pp. 1103–1109.
  12. E. Ramirez-Laboreo, M. G. L. Roes, and C. Sagues, “Hybrid Dynamical Model for Reluctance Actuators Including Saturation, Hysteresis and Eddy Currents,” IEEE/ASME Trans. Mechatronics, vol. 24, no. 3, pp. 1396–1406, jun 2019.
  13. N. H. Vrijsen, “Magnetic Hysteresis Phenomena in Electromagnetic Actuation Systems,” Ph.D. dissertation, Eindhoven University of Technology, 2014.
  14. E. Ramirez-Laboreo, “Modeling and Control of Reluctance Actuators,” Ph.D. dissertation, Universidad de Zaragoza, 2019.
  15. E. Ramirez-Laboreo and C. Sagues, “Reluctance actuator characterization via FEM simulations and experimental tests,” Mechatronics, vol. 56, pp. 58–66, dec 2018.
  16. E. Moya-Lasheras, E. Ramirez-Laboreo, and C. Sagues, “Probability-Based Optimal Control Design for Soft Landing of Short-Stroke Actuators,” IEEE Trans. Control Syst. Technol., 2019.
  17. J. A. Nelder and R. Mead, “A Simplex Method for Function Minimization,” Comput. J., vol. 7, no. 4, pp. 308–313, jan 1965.
  18. J. Tsai, C. R. Koch, and M. Saif, “Cycle Adaptive Feedforward Approach Controllers for an Electromagnetic Valve Actuator,” IEEE Trans. Control Syst. Technol., vol. 20, no. 3, pp. 738–746, may 2012.
  19. R. R. Ernst, “Measurement and Control of Magnetic Field Homogeneity,” Rev. Sci. Instrum., vol. 39, no. 7, pp. 998–1012, jul 1968.
  20. B. L. Nelson, W. D. Kelton, G. M. Clark, R. R. Barton, and S. John, “Modifications of the Nelder-Mead Simplex Method for Stochastic Simulation Response Optimization,” in 1991 Winter Simul. Conf., 1991, pp. 945–953.
Citations (15)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now