Automatic PID parameter tuning based on unfalsified control
Traditional robust control, or adaptive control, is based on accurate models. They can only control the systems with sufficiently small or constant uncertainty. To overcome the shortcoming, the paper proposes an unfalsified control based on data driving, which is a type of model-free adaptive control. The method is data-driven and thus does not rely on the system model. The designed controller is simple and highly adaptable to online application. In this paper, the basic theory of unfalsified control is introduced and applied to the real-time tuning and adaptation of the PID controller parameters. Simulation is also conducted when there is disturbance with the system. The result shows that the algorithm is actually fairly robust to noise and perturbation. The feasibility and effectiveness of this algorithm are also proved by the simulation results.
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Safonov M. G., Tsao T. C. The unfalsified control concept and learning [C]. Proceedings of the 33rd IEEE Conference on Decision and Control, IEEE, 1994, 3:2819-2824.
Paul A., Stefanovic M., Safonov M. G., et al. Multi-Controller Adaptive Control (MCAC) for a tracking problem using an unfalsification approach [C]. CDC-ECC'05 44th IEEE European Control Conference on Decision and Control, 2005:4815-4820.
Jun M., Safonov M. G. Automatic PID tuning: An application of unfalsified control [C]. Proceedings of the IEEE International Symposium on Computer Aided Control System Design, 1999:328-333.
Safonov M. G., Tsao T. C. The unfalsified control concept: A direct path from experiment to controller [M]. Feedback Control, Nonlinear Systems, and Complexity, Springer Berlin Heidelberg, 1995:196-214.
Wang R., Paul A., Stefanovic M., et al. Cost detectability and stability of adaptive control systems [J]. International Journal of Robust and Nonlinear Control, 2007, 17(5/6):549-561.
Wang R., Safonov M. G. Stability of unfalsified adaptive control using multiple controllers [C]. Proceedings of the American Control Conference, 2005:3162-3167.
Brozenec T. F., Tsao T. C., Safonov M. G. Controller validation [J]. International Journal of Adaptive Control and Signal Processing, 2001, 15(5):431-444.
Van Helvoort J. J. M. Unfalsified control: Data-driven control design for performance improvement [D]. Eindhoven: Technische Universiteit Eindhoven, 2007.
Manzar M. N., Battistelli G., Sedigh A. K. Input-constrained multi-model unfalsified switching control [J]. Automatica, 2017:391-395.
Markovsky I. The most powerful unfalsified model for data with missing values [J]. Systems & Control Letters, 2016, 95:53-61.
Jiang P., Cheng Y., Wang X., et al. Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking [J]. IEEE Transactions on Cybernetics, 2016, 46(12):3032.