Abstract
Active vibration suppression is a well explored area when it comes to simple problems, however as the problem complexity grows to a time variant system, the amount of researched solutions drops by a large margin, which is further increased with the added requirement of very limited knowledge about the controlled system. These conditions make the problem significantly more complicated, often rendering classic approaches suboptimal or unusable, requiring a more intelligent approach - such as utilizing soft computing. This work proposes a Artificial Neural Network (ANN) Model Predictive Control (MPC) scheme, inspired by horizon techniques which are used for MPC. The proposed approach aims to solve the problem of active vibration control of an unknown and largely unobservable time variant system, while attempting to keep the controller fast by introducing several methods of reducing the amount of calculations inside the control loop - which with proper tuning have no negative impact on the controller’s performance. The proposed approach outperforms the multi-input Proportional-Derivative (PD) controller preoptimized using a genetic algorithm.
The work presented in this paper was supported by the National Science Centre in Poland under the research project no. 2016/21/D/ST8/01678.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Camacho, E., Bordons, C., Alba, C.: Model Predictive Control. Advanced Textbooks in Control and Signal Processing. Springer, London (2004). https://books.google.pl/books?id=Sc1H3f3E8CQC
Dworakowski, Z., Mendrok, K.: Indirect structural change detection based on control algorithm’s performance. In: Proceedings of European Workshop on Structural Health Monitoring 2018, pp. 1–10 (2018). https://www.ndt.net/article/ewshm2018/papers/0024-Dworakowski.pdf
Hossain, M.S., et al.: Artificial neural networks for vibrationbased inverse parametric identifications: a review. Appl. Soft Comput. J. 52, 203–219 (2016). https://doi.org/10.1016/j.asoc.2016.12.014. http://linkinghub.elsevier.com/retrieve/pii/S1568494616306329
Landau, I., Lozano, R., M’Saad, M., Karimi, A.: Adaptive Control. Springer, London (2011). https://doi.org/10.1007/978-0-85729-664-1
Landau, I.D., Lozano, R., M’Saad, M.: Adaptive Control. Springer, Heidelberg (1998). https://doi.org/10.1007/978-0-85729-343-5
Leeghim, H., Kim, D.: Adaptive neural control of spacecraft using controlmoment gyros. Adv. Space Res. 55(5), 1382–1393 (2015). https://doi.org/10.1016/j.asr.2014.06.038
Mal’tsev, A.A., Maslennikov, R., Khoryaev, A., Cherepennikov, V.: Adaptive active noise and vibration control. Acoust. Phys. 51(2), 195–208 (2005)
Milovanović, M.B., Antić, D.S., Milojković, M.T., Nikolić, S.S., Perić, S.L., Spasić, M.D.: Adaptive PID control based on orthogonal endocrine neural networks. Neural Netw. 84, 80–90 (2016). https://doi.org/10.1016/j.neunet.2016.08.012
Muhammad, B.B., Wan, M., Feng, J., Zhang, W.H.: Dynamic damping of machiningvibration: a review. Int. J. Adv. ManufacturingTechnology (2016). https://doi.org/10.1007/s00170-016-9862-z
Pan, Y., Er, M.J., Sun, T., Xu, B., Yu, H.: Adaptive fuzzy PD control with stable H tracking guarantee. Neurocomputing (2016). https://doi.org/10.1016/j.neucom.2016.08.091
Preumont, A.: Vibration Control of Active Structures: An Introduction. Solid Mechanics and Its Applications. Springer, Netherlands (2011). https://doi.org/10.1007/978-94-007-2033-6. https://books.google.pl/books?id=MUQUQyB4bEUC
Rao, S.S., Fah, Y.F.: Mechanical Vibrations; 5th edn. in SI units. Prentice Hall, Singapore (2011). https://cds.cern.ch/record/1398617
Subasri, R., Suresh, S., Natarajan, A.M.: Discrete direct adaptive ELM controller for active vibration control of nonlinear base isolation buildings. Neurocomputing 129, 246–256 (2014). https://doi.org/10.1016/j.neucom.2013.09.035
Terasawa, T., Sano, A.: Fully adaptive semi-active control of vibration isolation by Mr Damper. IFAC 38 (2002). https://doi.org/10.3182/20050703-6-CZ-1902.00254
Valoor, M., Chandrashekhara, K., Agarwal, S.: Self-adaptive vibration controlof smart composite beams using recurrent neural architecture. Int. J. Solids Struct. 38(44–45), 7857–7874 (2001). https://doi.org/10.1016/S0020-7683(01)00125-1. http://linkinghub.elsevier.com/retrieve/pii/S0020768301001251
Zak, S.H.: Systems and Control. Oxford University Press, Oxford (2002)
Zhao, Z.l., Qiu, Z.c., Zhang, X.m., Han, J.d.: Vibration control of a pneumatic driven piezoelectric flexible manipulator using self-organizing map based multiple models. Mech. Syst. Signal Process. 70–71, 345–372 (2016). https://doi.org/10.1016/j.ymssp.2015.09.041, http://linkinghub.elsevier.com/retrieve/pii/S0888327015004537, www.sciencedirect.com/science/article/pii/S0888327015004537
Acknowledgement
The work presented in this paper was supported by the National Science Centre in Poland under the research project no. 2016/21/D/ST8/01678.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Heesch, M., Dworakowski, Z. (2019). Neural Net Model Predictive Controller for Adaptive Active Vibration Suppression of an Unknown System. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-20912-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20911-7
Online ISBN: 978-3-030-20912-4
eBook Packages: Computer ScienceComputer Science (R0)