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A Hybrid Algorithm for Iterative Adaptation of Feedforward Controllers: an Application on Electromechanical Switches (2404.00036v2)

Published 23 Mar 2024 in eess.SY and cs.SY

Abstract: Electromechanical switching devices such as relays, solenoid valves, and contactors offer several technical and economic advantages that make them widely used in industry. However, uncontrolled operations result in undesirable impact-related phenomena at the end of the stroke. As a solution, different soft-landing controls have been proposed. Among them, feedforward control with iterative techniques that adapt its parameters is a solution when real-time feedback is not available. However, these techniques typically require a large number of operations to converge or are computationally intensive, which limits a real implementation. In this paper, we present a new algorithm for the iterative adaptation that is able to eventually adapt the search coordinate system and to reduce the search dimensional size in order to accelerate convergence. Moreover, it automatically toggles between a derivative-free and a gradient-based method to balance exploration and exploitation. To demonstrate the high potential of the proposal, each novel part of the algorithm is compared with a state-of-the-art approach via simulation.

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References (12)
  1. R. Schroedter, M. Roth, K. Janschek, and T. Sandner, “Flatness-based open-loop and closed-loop control for electrostatic quasi-static microscanners using jerk-limited trajectory design,” Mechatronics, vol. 56, pp. 318–331, 2018.
  2. M. Grotjahn and B. Heimann, “Model-based feedforward control in industrial robotics,” The International Journal of Robotics Research, vol. 21, no. 1, pp. 45–60, 2002.
  3. S.-S. Yeh and P.-L. Hsu, “An optimal and adaptive design of the feedforward motion controller,” IEEE/ASME transactions on mechatronics, vol. 4, no. 4, pp. 428–439, 1999.
  4. E. Moya-Lasheras, E. Ramirez-Laboreo, and E. Serrano-Seco, “Run-to-Run Adaptive Nonlinear Feedforward Control of Electromechanical Switching Devices,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 5358–5363, 2023, 22nd IFAC World Congr.
  5. R. M. Lewis and V. Torczon, “Pattern search methods for linearly constrained minimization,” SIAM J. Optimization, vol. 10, no. 3, pp. 917–941, 2000.
  6. E. Ramirez-Laboreo, E. Moya-Lasheras, and E. Serrano-Seco, “Faster run-to-run feedforward control of electromechanical switching devices: a sensitivity-based approach,” in in Proc. Eur. Control Conf., Stockholm, Sweden, June 2024.
  7. I. Loshchilov, M. Schoenauer, and M. Sebag, “Adaptive coordinate descent,” in Prod. 13th GECCO, 2011, pp. 885–892.
  8. N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evolutionary computation, vol. 9, no. 2, pp. 159–195, 2001.
  9. E. Moulay, V. Léchappé, and F. Plestan, “Properties of the sign gradient descent algorithms,” Information Sciences, vol. 492, pp. 29–39, 2019.
  10. X. Wang, M. Johansson, and T. Zhang, “Generalized polyak step size for first order optimization with momentum,” in International Conference on Machine Learning.   PMLR, 2023, pp. 35 836–35 863.
  11. N. Loizou, S. Vaswani, I. H. Laradji, and S. Lacoste-Julien, “Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence,” in International Conference on Artificial Intelligence and Statistics.   PMLR, 2021, pp. 1306–1314.
  12. J. Lévine, “On necessary and sufficient conditions for differential flatness,” Appl. Algebra Eng., Commun. Comput., vol. 22, no. 1, pp. 47–90, 2011.

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