Spiking PID Control Applied in the Van de Vusse Reaction

(1) Carlos Antonio Márquez-Vera Mail (Universidad Veracruzana, Mexico)
(2) Zaineb Yakoub Mail (National Engineering School of Gabès, Tunisia)
(3) * Marco Antonio Márquez Vera Mail (Polytechnic University of Pachuca, Mexico)
(4) Alfian Ma'arif Mail (Universitas Ahmad Dahlan, Indonesia)
*corresponding author


Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN.  The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.


Spiking Neuron; PID Control; Chemical Reaction; Van de Vusse Reaction; Artificial Neural Networks




Article metrics

10.31763/ijrcs.v1i4.490 Abstract views : 2684 | PDF views : 506




Full Text



[1] S. R. Nandakumar, S. R. Kulkarni, A. V. Babu and B. Rajendran, “Building brain-inspired computing systems: Examining the role of nanoscale devices,” IEEE Nanotechnology Magazine, vol. 13, no. 2, pp. 19-35, Sep. 2018. https://doi.org/10.1109/MNANO.2018.2845078

[2] J. C. Patterson, Managing a real-time massively-parallel neural architecture, Doctoral Thesis. University of Manchester, School of Computer Science, 232 pages, 2012.

[3] J. Feng and S. Lu, “Performance analysis of various activation functions in artificial neural networks,” J. Phys.: Conf. Series, vol. 1237, 022030, 2019. https://doi.org/10.1088/1742-6596/1237/2/022030

[4] J. Ou, Y. Li and W. Liu, “TDP: Two-dimensional perceptron for image recognition,” Knowledge-Based Systems, vol. 195, pp. 105615, May. 2020. http://dx.doi.org/10.1016/j.knosys.2020.105615

[5] Y. V. Tiumentsev and M. V. Egorchev, Chapter 4 - Neural network black box modeling of nonlinear dynamical systems: Aircraft controlled motion. Neural Network Modeling and Identification of Dynamical Systems, Academic Press, pp. 131-163, 2019. http://dx.doi.org/10.1016/B978-0-12-815254-6.00014-9

[6] Y. LeCun, Y. Bengio and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015. https://doi.org/10.1038/nature14539X

[7] F.C. Morabito, M. Campolo, C. Ieracitano and N. Mammone, Chapter 11 - Deep Learning Approaches to Electrophysiological Multivariate Time-Series Analysis, Artificial Intelligence in the Age of Neural Networks and Brain Computing, Academic Press, pp. 219-243, 2019. https://doi.org/10.1016/B978-0-12-815480-9.00011-6

[8] G. Liu, H. Bao and B. Han, “A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis,” Mathematical Problems in Engineering, Hindawi, vol. 2018, article 5105709, 10 pages, July 2018. https://doi.org/10.1155/2018/5105709

[9] S. Indolia, A. K. Goswamim, S. P. Mishra and P. Asopa, “Conceptual understanding of convolutional neural network - A deep learning approach,” Procedia Computer Science, vol. 132, pp. 679-688, 2018. https://doi.org/10.1016/j.procs.2018.05.069

[10] B. J. Wythoff, “Backpropagation neural networks: A tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 18, pp. 115-155, 1993. https://doi.org/10.1016/0169-7439(93)80052-J

[11] E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1569-1572, Nov. 2003. https://doi.org/10.1109/TNN.2003.820440

[12] M. S. Asghar, S. Arslan and H. Kim, “A low-power spiking neural network chip based on a compact LIF neuron and binary exponential charge injector synapse circuits,” Sensors, vol. 21, 4462, 17 pages, June 2021. https://doi.org/10.3390/s21134462

[13] S. Zhou, X. Li, Y. Chen, S. T. Chandrasekaran and A. Sanyal, “Temporal-coded deep spiking neural network with easy training and robust performance,” in proc. The Thirty-Fifth AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 2021, pp. 11143-11151. https://ojs.aaai.org/index.php/AAAI/article/view/17329

[14] H. Perez, B. Ogunnaike and S. Devasia, “Output tracking between operating points for nonlinear processes: Van de Vusse example,” IEEE Transactions on Control Systems Technology, vol. 10, 2002, pp. 611–617. https://doi.org/10.1109/TCST.2002.1014680

[15] P. Patil and C. S. Rao, "Enhanced PID controller for non-minimum phase second order plus time delay system," Chemical Product and Process Modeling, vol. 14, no. 3, pp. 20180059, 2019. https://doi.org/10.1515/cppm-2018-0059

[16] V. Alfaro, P. Balaguer and O. Arrieta, “Robustness considerations on PID tuning for regulatory control of inverse response processes,” in: Elsevier (Ed.), IFAC Conference on advanced PID Control, Bresia, Italy, 2012, pp. 193–198. https://doi.org/10.3182/20120328-3-IT-3014.00033

[17] S. Kuntanapreeda and P. Marusak, “Nonlinear extended output feedback control for CSTRs with Van de Vusse reaction,” Computers & Chemical Engineering, vol. 41, pp. 10–23, 2012. https://doi.org/10.1016/j.compchemeng.2012.02.010

[18] A. Bertone, R. da M. Jafelice and B. Goes, “Classic and fuzzy type-2 control for the Van de Vusse reactor: A comparative study,” in: S. de Matemática Aplicada e Computacional (Ed.), Proc. Series of the Brazilian Society of Computational and Applied Mathematics, vol. 6, Sao Carlos, Brazil, Dec. 2018, pp. 1–7. https://doi.org/10.5540/03.2018.006.02.0258

[19] R. Malar and T. Thyagarajan, “Artificial neural networks based modeling and control of continuous stirred tank reactor,” American J. of Engineering and Applied Sciences, vol. 2, pp. 229–235, 2009. https://thescipub.com/pdf/ajeassp.2009.229.235.pdf

[20] R. Batllori, C. B. Laramee, W. Land and J. D. Schaffer,” Evolving spiking neural networks for robot control,” Procedia Computer Science, vol. 6, pp. 329-334, 2011. https://doi.org/10.1016/j.procs.2011.08.060

[21] R. Stagsted, A. Vitale, J. Binz, A. Renner, L. Bonde, Leon and Y. Sandamirskaya, “Towards neuromorphic control: A spiking neural network based PID controller for UAV,” In: Robotics: Science and Systems, Virtual Conference, 12-16 July 2020. https://doi.org/10.15607/rss.2020.xvi.074

[22] J. Pérez, J. A.CabreraJuan, J. Castillo and J. M. Velasco, “Bio-inspired spiking neural network for nonlinear systems control,” Neural Networks, vol. 104, pp. 15-25, 2018. https://doi.org/10.1016/j.neunet.2018.04.002

[23] J. Wang, Spiking Neural P Systems, Doctoral Thesis. Faculty of Science, Leiden University, the Netherlands, IPA Dissertation Series, 169 pages, Dec. 2011. https://scholarlypublications.universiteitleiden.nl/handle/1887/18261

[24] S. Engell and K. Klatt, “Nonlinear control of a nonminimum-phase CSTR,” in: IEEE (Ed.), Proceedings if the American Control Conference, San Francisco, United States of America, 1993, pp. 2941–2945. https://doi.org/10.23919/acc.1993.4793439

[25] M.A. Márquez-Vera, L.E. Ramos-Velasco and B.D. Balderrama-Hernández, “Stable fuzzy control and observer via LMIs in a fermentation process,” Journal of Computational Science, vol. 27, pp. 192–198, 2018. https://doi.org/10.1016/j.jocs.2018.06.002

[26] E. Bayro-Corrochano, L. Lechuga-Gutiérrez and M. Garza-Burgos, “Geometric techniques for robotics and HMI: Interpolation and haptics in conformal geometric algebra and control using quaternion spike neural networks,” Robotics and Autonomous Systems, vol. 104, pp. 72-84, 2018. https://doi.org/10.1016/j.robot.2018.02.015


  • There are currently no refbacks.

Copyright (c) 2021 Carlos Antonio Márquez-Vera, Zaineb Yakoub, Marco Antonio Márquez Vera, Alfian Ma'Arif

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


About the JournalJournal PoliciesAuthor Information

International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE)Peneliti Teknologi Teknik IndonesiaDepartment of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia