Abstract
This article presents a comparison between a common type III controller and one based on a brain emotional learning paradigm (BELBIC) parameterized using a particle swarm optimization algorithm (PSO). Both strategies were evaluated regarding the set-point accuracy, disturbances rejection ability and control effort of a DC-DC buck converter. The simulation results suggests that, when compared to the common controller, the BELBIC leads to an increase in both set-point tracking and disturbances rejection ability while reducing the dynamics of the control signal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sankarganesh, R., Thangavel, S.: Performance analysis of various DC-DC converters with optimum controllers for PV applications. Res. J. Appl. Sci. Eng. Technol. 8, 929–941 (2014)
Khorashadizadeh, S., Mahdian, M.: Voltage tracking control of DC-DC boost converter using brain emotional learning. In: 4th International Conference on Control, Instrumentation, and Automation (ICCIA), pp. 268–272 (2016)
Erickson, R.W., Maksimovic, D.: Fundamentals of Power Electronics, 2nd edn. Springer, Boston (2001). https://doi.org/10.1007/b100747
Sarpeshkar, R.: Neuromorphic and Biomorphic Engineering Systems. McGraw-Hill Yearbook of Science and Technology. McGraw-Hill, New York (2009)
Balkenius, C., Morén, J.: A computational model of emotional learning in the amygdala. Cybern. Syst. 32(6), 611–636 (2001)
Lucas, C., Shahmirzadi, D., Sheikholeslami, N.: Introducing BELBIC: brain emotional learning based intelligent controller. Intell. Autom. Soft Comput. 10, 11–22 (2004)
Rouhani, H., Jalili, M., Araabi, B.N., Eppler, W., Lucas, C.: Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger. Expert Syst. Appl. 32(3), 911–918 (2007)
Rahman, M.A., Milasi, R.M., Lucas, C., Araabi, B.N., Radwan, T.S.: Implementation of emotional controller for interior permanent-magnet synchronous motor drive. IEEE Trans. Ind. Appl. 44(5), 1466–1476 (2008)
Nahian, S.A., Truong, D.Q., Ahn, K.K.: A self-tuning brain emotional learning based intelligent controller for trajectory tracking of electrohydraulic actuator. J. Syst. Control Eng. 228, 461–475 (2014)
Coelho, J.P., Pinho, T.M., Boaventura-Cunha, J., de Oliveira, J.B.: A new brain emotional learning Simulink \(\textregistered \) toolbox for control systems design. IFAC-PapersOnLine 50, 16009–16014 (2017)
Jafarzadeh, S., Jahed Motlagh, M.R., Barkhordari, M., Mirheidari, R.: A new Lyapunov based algorithm for tuning BELBIC controllers for a group of linear systems. In: 2008 16th Mediterranean Conference on Control and Automation. IEEE, June 2008
Garmsiri, N., Najafi, F.: Fuzzy tuning of brain emotional learning based intelligent controllers. In: 2010 8th World Congress on Intelligent Control and Automation. IEEE, July 2010
Jafari, M., Mohammad Shahri, A., Hamid Elyas, S.: Optimal tuning of brain emotional learning based intelligent controller using clonal selection algorithm. In: ICCKE 2013. IEEE, October 2013
Valizadeh, S., Jamali, M.-R., Lucas, C.: A particle-swarm-based approach for optimum design of BELBIC controller in AVR system. In: International Conference on Control, Automation and Systems, COEX, Seoul, Korea, pp. 2679–2684, October 2008
Valipour, M.H., Maleki, K.N., Ghidary, S.S.: Optimization of emotional learning approach to control systems with unstable equilibrium. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SCI, vol. 569, pp. 45–56. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10389-1_4
El-Saify, M.H., El-Garhy, A.M., El-Sheikh, G.A.: Brain emotional learning based intelligent decoupler for nonlinear multi-input multi-output distillation columns. Math. Probl. Eng. 1–13, 2017 (2017)
Mei, Y., Tan, G., Liu, Z.: An improved brain-inspired emotional learning algorithm for fast classification. Algorithms 10(2), 70 (2017)
César, M.B., Coelho, J.P., Gonalves, J.: Evolutionary-based bel controller applied to a magneto-rheological structural system. Actuators 7(2), 29 (2018)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Network, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0040810
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Coelho, J.P., Braz-César, M., Gonçalves, J. (2021). BELBIC Based Step-Down Controller Design Using PSO. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-91885-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91884-2
Online ISBN: 978-3-030-91885-9
eBook Packages: Computer ScienceComputer Science (R0)