NEUROEVOLUTIONARY APPROACH TO CONTROL OF COMPLEX MULTICOORDINATE INTERRELATED PLANTS

Authors

  • Yuriy Kondratenko
  • Oleksiy Kozlov
  • Oleksandr Gerasin

DOI:

https://doi.org/10.47839/ijc.18.4.1620

Keywords:

multicoordinate interrelated plant, automatic control system, neural controller, neural network, neuroevolution, genetic algorithm, caterpillar mobile robot.

Abstract

This paper presents the developed by the authors step-by-step neuroevolution based approach to designing control systems for complex multicoordinate interrelated plants (MIP). The proposed approach allows us to build the structure of the automatic control system (ACS) for the MIP on the basis of the single complex neural controller (NC) with multiple inputs and outputs as well as to implement the effective training of its multilayer neural network (NN) by means of the evolutionary based algorithm, taking into account the mutual influence of all variables of the MIP in an optimal way. In order to study and validate the efficiency of the presented approach the design of the ACS for the spatial motion of caterpillar mobile robot (MR) able to move on inclined and vertical ferromagnetic surfaces is carried out in this work. The developed ACS based on the NC with optimal structure allows us to achieve high quality indicators of spatial motion control, taking into account the mutual influence of control channels of the MR’s speed and angle that confirms the high efficiency of the proposed approach.

References

B.R. Mehta, Y.J. Reddy, “Chapter 7 - SCADA systems,” Industrial Process Automation Systems, pp. 237-300, 2015.

Y.P. Kondratenko, O.V. Kozlov, O.V. Korobko, A.M. Topalov, “Internet of Things approach for automation of the complex industrial systems,” Proceedings of the 13th International Conference ICTERI’2017, CEUR-WS, Ermolayev, V. et al. (Eds), Vol-1844, Kyiv, Ukraine, 2017, pp. 3-18.

H. Merz, T. Hansemann, C. Hübner, “Building automation: communication systems with EIB/KNX, LON and BACnet,” Signals and Communication Technology, Berlin, Heidelberg: Springer-Verlag, 2009, 308 p.

Y.P. Kondratenko, O.V. Kozlov, O.S. Gerasin, A.M. Topalov, O.V. Korobko, “Automation of control processes in specialized pyrolysis complexes based on Web SCADA systems,” Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 1, Bucharest, Romania, pp. 107-112, 2017.

J. Kacprzyk, Multistage Fuzzy Control: A Prescriptive Approach, John Wiley & Sons, Inc., New York, NY, USA, 1997.

B. Ross, J. Bares, C. Fromme, “A semi-autonomous robot for stripping paint from large vessels,” Int. J. of Robotics Research, pp. 617-626, 2008.

Y.P. Kondratenko, O.V. Kozlov, “Mathematic modeling of reactor’s temperature mode of multiloop pyrolysis plant,” Modeling and Simulation in Engineering, Economics and Management, Lecture Notes in Business Information Processing, vol. 115, pp. 178-187, 2012.

B. Siciliano, O. Khatib (Eds), Springer Handbook of Robotics. Springer, 2016.

M. W. Spong, S. Hutchinson, M. Vidyasagar, Robot Modeling and Control, New York: Publisher Wiley, 2006.

N. K. Bose, “Applied multidimensional systems theory,” Springer International Publishing AG, Springer, Cham, 2017, 192 p.

E. Rogers, K. Galkowski, W. Paszke, K. L. Moore, P. H. Bauer, L. Hladowski, P. Dabkowski, “Multidimensional control systems: case studies in design and evaluation,” Multidimensional Systems and Signal Processing, vol. 26, issue 4, pp. 895–939, 2015.

D. Souto, A. Faiña, F. Lуpez-Peсa, R. J. Duro, “Lappa: a new type of robot for underwater non-magnetic and complex hull cleaning,” IEEE Int. Conf. on Robotics and Automation (ICRA), Karlsruhe, 2013, pp. 3394-3399.

L. Christensen, N. Fischer, S. Kroffke, J. Lemburg, R. Ahlers, “Cost-effective autonomous robots for ballast water tank inspection,” J. of Ship Production and Design, August, vol. 27, no. 3, pp. 127-136, 2011.

M.T. Hayajneh, S.M. Radaideh, I.A. Smadi, “Fuzzy logic controller for overhead cranes,” Engineering Computations, vol. 23, issue 1, pp. 84-98, 2006.

L.A. Zadeh, A.M. Abbasov, R.R. Yager, S.N. Shahbazova, M.Z. Reformat, Eds., “Recent developments and new directions in soft computing,” STUDFUZ 317, Cham: Springer, 2014, 466 p.

M. Jamshidi, V. Kreinovich, J. Kacprzyk, Eds., “Advance trends in soft computing,” STUDFUZ 312, Cham: Springer-Verlag, 2013, 468 p.

S.O. Subbotin, A.O. Oliinyk, O.O. Oliinyk, Non-iterative, Evolutionary and Multi-agent Methods for Synthesizing Fuzzy and Neural Network Models, Zaporizhzhia: ZNTU, 2009. 375 p.

A. Piegat, “Fuzzy modeling and control,” Vol. 69, Physica, 2013, 704 p.

Z. Xiao, J. Guo, H. Zeng, P. Zhou, S. Wang, “Application of fuzzy neural network controller in hydropower generator unit,” J. Kybernetes, vol. 38, no. 10, pp. 1709-1717, 2009.

Y.P. Kondratenko, J. Rudolph, O.V. Kozlov, Y.M. Zaporozhets, O.S. Gerasin, “Neuro-fuzzy observers of clamping force for magnetically operated movers of mobile robots,” Technical Electrodynamics, no. 5, pp. 53-61, 2017. (in Ukrainian)

R. Hampel, M. Wagenknecht, N. Chaker, “Fuzzy Control: Theory and Practice,” New York: Physika-Verlag, Heidelberg, 2000.

Y. Zhang, Ch. Yingliu, X. Song, Zh. Yan, “Application of RBF neural network PID controller in the rectification column temperature control system,” Proceedings of the Sixth Int. Symp. on Computational Intelligence and Design, vol. 2, 2013, pp. 72-75.

A.I. Glushchenko, V.A. Petrov, “On neural tuner application to adjust speed P-controller of rolling mill main DC drive,” Proceedings of the International Siberian Conference on Control and Communications (SIBCON), 2017, pp. 1-5.

L.U. Emaletdinova, A.N. Kabirova, “Neural fuzzy controller to control the angle of heel and the course of the unmanned aerial vehicle,” Proceedings of the International Conference on Dynamics of Systems, Mechanisms and Machines (Dynamics), 2016, pp. 1-5.

J.K. Molina, M.J. Dominguez, C.U. Onate, H.T. Salamea, “Development of a neural controller applied in a 5 DOF robot redundant,” IEEE Latin America Transactions, vol. 12, issue 2, pp. 98-106, 2014.

W. Yaoyao; L. Tianlin; D. Yian, “Learning to chase a ball efficiently and smoothly for a wheeled robot,” Proceedings of the 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, New Zealand, Nov 21-23, 2017, p. 36-41.

B.M. Zaineb, A. Aicha, B.H. Mouna, S. Lassaad, “Speed control of DC motor based on an adaptive feed forward neural IMC controller,” Proceedings of the 2017 Int. Conf. on Green Energy Conversion Systems (GECS), 2017, pp. 1-7.

S.K. Suman, M.K. Gautam, R. Srivastava, V.K. Giri, “Novel approach of speed control of PMSM drive using neural network controller,” Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT’2016), 2016, pp. 2780-2783.

M. Mohammadi, A. Nikbakht, A. Bavalishoar, “Fuel oil leak detection in power plant with recurrent neural network and execute in programmable logic controller,” Proceedings of the 2015 2nd Int. Conf. on Knowledge-Based Engineering and Innovation (KBEI), 2015, pp. 927-932.

S. Kamalasadan, G.D. Swann, R. Yousefian, “A novel system-centric intelligent adaptive control architecture for power system stabilizer based on adaptive neural networks,” IEEE Systems Journal, vol. 8, issue 4, pp. 1074-1085, 2014.

Y.I. Eremenko, A.I. Glushchenko, A.V. Fomin, “On PI-Controller neural tuner implementation in programmable logic controller to improve rejection of disturbances effecting heating plant,” Proceedings of the 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 2017, pp. 1-5.

T. Sunil, M. Jayachandra, R. Prasad, “Control of ship's roll using active fin stabilizers and neural network controller,” Proceedings of the 2016 Int. Conf. on Advanced Communication Control and Computing Technologies (ICACCCT), 2016, pp. 516-519.

B. Xu, Ch. Yang, Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, issue 10, pp. 2563-2575, 2015.

E. Bejar and A. Moran, "Deep reinforcement learning based neuro-control for a two-dimensional magnetic positioning system," 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, 2018, pp. 268-273.

D. Simon, Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence, John Wiley & Sons, 2013, 772 p.

A. Smiley, D. Simon, “Evolutionary optimization of atrial fibrillation diagnostic algorithms,” Int. J. of Swarm Intelligence, vol. 2, nos. 2/3/4, pp. 117-133, 2016.

K.O. Stanley, J. Clune, J. Lehman, R. Miikkulainen, “Designing neural networks through neuroevolution,” Nature Machine Intelligence 1, pp. 24-35, 2019.

S. Rodzin, O. Rodzina, L. Rodzina, “Neuroevolution: Problems, algorithms, and experiments,” Proceedings of the 2016 IEEE 10th Int. Conf. on Application of Information and Communication Technologies (AICT), 2016, pp. 1-4.

J. Bower, M. Shahverdi, D. Blekhman, “Neuroevolution based optimization of hybrid transmission shift points,” Proceedings of the 2018 IEEE Conference on Technologies for Sustainability (SusTech), 2018, pp. 1-5.

D.V. Vargas, J. Murata, “Spectrum-diverse neuroevolution with unified neural models,” IEEE Trans. on Neural Networks and Learning Systems, vol. 28, issue 8, pp. 1759-1773, 2017.

M.-K. Jiau, Sh.-Ch. Huang, “Self-organizing neuroevolution for solving carpool service problem with dynamic capacity to alternate matches,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, issue 4, pp. 1048-1060, 2019.

S.K. Oh, W. Pedrycz, “The design of hybrid fuzzy controllers based on genetic algorithms and estimation techniques,” J. Kybernetes, vol. 31, no. 6, pp. 909-917, 2002.

M. Colby, L. Yliniemi, P. Pezzini, D. Tucker, K.M. Bryden, K. Tume, “Multiobjective neuroevolutionary control for a fuel cell turbine hybrid energy system,” Proceedings of the Genetic and Evolutionary Computation Conference GECCO’16, Denver, Colorado, USA, 2016, pp. 877-884.

J. Gea, F. Kirchner, “Using neuroevolution for optimal impedance control,” Proceedings of the 2008 IEEE Int. Conf. on Emerging Technologies and Factory Automation, 2008, pp. 1063-1066.

S.D. Prestwich, S.A. Tarim, R. Rossi, B. Hnich, “A neuroevolutionary approach to stochastic inventory control in multi-echelon systems,” International Journal of Production Research, vol. 50, issue 8, pp. 2150-2160, 2012.

B. Trasnea, L.A. Marina, A. Vasilcoi, C.R. Pozna, S.M. Grigorescu, “GridSim: A vehicle kinematics engine for deep neuroevolutionary control in autonomous driving,” Proceedings of the 2019 Third IEEE Int. Conf. on Robotic Computing (IRC), 2019, pp. 443-444.

N.K. Bose, B. Buchberger, J.P. Guiver, Multidimensional Systems Theory and Applications, Berlin: Springer, 2003, 268 p.

D. Souto, A. Faiña, A. Deibe, F. Lopez-Peña, R. J. Duro, “A robot for the unsupervised grit-blasting of ship hulls,” Int. J. of Advanced Robotic Systems, vol. 9, no. 82, pp. 1-16, 2012.

D. Longo, G. Muscato, “A small low-cost low-weight inspection robot with passive-type locomotion,” Integrated Computer-Aided Engineering, vol. 11, pp. 339-348, 2004.

Y. Kondratenko, Y. Zaporozhets, J. Rudolph, O. Gerasin, A. Topalov, O. Kozlov, “Modeling of clamping magnets interaction with ferromagnetic surface for wheel mobile robots,” International Journal of Computing, vol. 17, issue 1, pp. 33-46, 2018.

Y.P. Kondratenko, Y.M. Zaporozhets, J. Rudolph, O.S. Gerasin, A.M. Topalov, O.V. Kozlov, “Features of clamping electromagnets using in wheel mobile 1robots and modeling of their interaction with ferromagnetic plate,” Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Bucharest, Romania, 2017, vol. 1, pp. 453-458.

Downloads

Published

2019-12-31

How to Cite

Kondratenko, Y., Kozlov, O., & Gerasin, O. (2019). NEUROEVOLUTIONARY APPROACH TO CONTROL OF COMPLEX MULTICOORDINATE INTERRELATED PLANTS. International Journal of Computing, 18(4), 502-514. https://doi.org/10.47839/ijc.18.4.1620

Issue

Section

Articles