Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator

  • M. ElsisiEmail author
Original Article


This paper proposes the neural network (NN) predictive controller that combines the advantages of NN and predictive control for the automatic voltage regulator (AVR). The NN predictive controller is suggested as a new intelligence controller rather than the conventional controllers for the AVR. This is the first application of the NN predictive controller for AVR. There are five parameters of the NN predictive controller which need a proper tuning to get a good performance by using the NN predictive controller. In recent papers, the parameters of NN predictive controller are typically set by trial and error or by the designer’s expertise. The imperialist competitive algorithm (ICA) is introduced in this paper as a new artificial intelligence technique instead of the trial-and-error or the designer’s expertise methods to get the optimal parameters of NN predictive controller in order to overcome the deviations of the voltage. The performance of the designed NN predictive controller based on the ICA is compared with the designed NN predictive controller based on the genetic algorithm and the conventional proportional–integral–derivative controller based on Ziegler–Nichols technique. The comparison emphasizes the superiority of the suggested NN predictive controller based on the ICA.


Imperialist competitive algorithm (ICA) Automatic voltage regulator (AVR) Neural network (NN) predictive controller 

List of symbols


The minimal prediction horizon of the output


The maximal prediction horizon of the output


The control horizon


Tentative control signal


The target response


The network model response


The weight of the control signal


A number > 1


The distance between colony and imperialist


A limit angle


The reference voltage


The output terminal voltage


The error signal


The control signal


The amplifier gain


The amplifier time constant


The exciter gain


The exciter time constant


The generator gain


The generator time constant


The sensor gain


The sensor time constant


Compliance with ethical standards

Conflict of interest

Author states that there are no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Kundur P (1994) Power system stability and control. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Saadat H (2002) Power system analysis. Tata Mcgraw-Hill, New DelhiGoogle Scholar
  3. 3.
    Chatterjee S, Mukherjee V (2016) PID controller for automatic voltage regulator using teaching–learning based optimization technique. Int J Electr Power Energy Syst 77:418–429CrossRefGoogle Scholar
  4. 4.
    Hasanien HM (2013) Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Syst J 7(4):825–831CrossRefGoogle Scholar
  5. 5.
    Devaraj D, Selvabala B (2009) Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system. IET Gener Transm Distrib 3(7):641–649CrossRefGoogle Scholar
  6. 6.
    Kansit S, Assawinchaichote W (2016) Optimization of PID controller based on PSOGSA for an automatic voltage regulator system. Procedia Comput Sci 86:87–90CrossRefGoogle Scholar
  7. 7.
    Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391CrossRefGoogle Scholar
  8. 8.
    Panda S, Sahu BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Frankl Inst 349(8):2609–2625MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Chatterjee A, Mukherjee V, Ghoshal SP (2009) Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system. Int J Electr Power Energy Syst 31(7):323–333CrossRefGoogle Scholar
  10. 10.
    Kim DH, Cho JH (2006) A biologically inspired intelligent PID controller tuning for AVR systems. Int J Control Autom Syst 4(5):624–636Google Scholar
  11. 11.
    Dos Santos Coelho L (2009) Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons Fractals 39(4):1504–1514CrossRefGoogle Scholar
  12. 12.
    Aguila-Camacho N, Duarte-Mermoud MA (2013) Fractional adaptive control for an automatic voltage regulator. ISA Trans 52(6):807–815CrossRefGoogle Scholar
  13. 13.
    Prasad LB, Gupta HO, Tyagi B (2014) Application of policy iteration technique based adaptive optimal control design for automatic voltage regulator of power system. Int J Electr Power Energy Syst 63:940–949CrossRefGoogle Scholar
  14. 14.
    Zhang H, Shi F, Liu Y (2014) Enhancing optimal excitation control by adaptive fuzzy logic rules. Int J Electr Power Energy Syst 63:226–235CrossRefGoogle Scholar
  15. 15.
    Hasan AR, Martis TS, Ula AS (1994) Design and implementation of a fuzzy controller based automatic voltage regulator for a synchronous generator. IEEE Trans Energy Convers 9(3):550–557CrossRefGoogle Scholar
  16. 16.
    Li H, Li F, Xu Y, Rizy DT, Kueck JD (2010) Adaptive voltage control with distributed energy resources: algorithm, theoretical analysis, simulation, and field test verification. IEEE Trans Power Syst 25(3):1638–1647CrossRefGoogle Scholar
  17. 17.
    Mao C, Malik OP, Hope GS, Fan J (1990) An adaptive generator excitation controller based on linear optimal control. IEEE Trans Energy Convers 5(4):673–678CrossRefGoogle Scholar
  18. 18.
    Camacho E, Bordons C (2004) Model predictive control. Springer, BerlinzbMATHGoogle Scholar
  19. 19.
    Farina M, Guagliardi A, Mariani F, Sandroni C, Scattolini R (2015) Model predictive control of voltage profiles in MV networks with distributed generation. Control Eng Pract 34:18–29CrossRefGoogle Scholar
  20. 20.
    Amraee T, Ranjbar AM, Feuillet R (2011) Adaptive under-voltage load shedding scheme using model predictive control. Electr Power Syst Res 81(7):1507–1513CrossRefGoogle Scholar
  21. 21.
    Kassem AM, Yousef AM (2013) Voltage and frequency control of an autonomous hybrid generation system based on linear model predictive control. Sustain Energy Technol Assess 4:52–61Google Scholar
  22. 22.
    Hassan LH, Moghavvemi M, Almurib HA, Steinmayer O (2013) Current state of neural networks applications in power system monitoring and control. Int J Electr Power Energy Syst 51:134–144CrossRefGoogle Scholar
  23. 23.
    Kassem AM (2010) Neural predictive controller of a two-area load frequency control for interconnected power system. Ain Shams Eng J 1(1):49–58CrossRefGoogle Scholar
  24. 24.
    Lachman T, Mohamad TR (2009) Neural network excitation control system for transient stability analysis of power system. In: TENCON 2009-2009 IEEE Region 10 Conference. IEEE, pp 1–6Google Scholar
  25. 25.
    Bahmanyar AR, Karami A (2014) Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs. Int J Electr Power Energy Syst 58:246–256CrossRefGoogle Scholar
  26. 26.
    Suykens JAK (1996) Artificial neural networks for modelling and control of non-linear systems, 1st edn. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  27. 27.
    Fausett L (1994) Fundamentals of neural networks, architectures, algorithms and applications, 2nd edn. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  28. 28.
    Yan G, Li C (2011) An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. J Comput Inf Syst 7(9):3309–3316Google Scholar
  29. 29.
    Abachizadeh M, Yazdi MRH, Yousefi-Koma A (2010) Optimal tuning of PID controllers using artificial bee colony algorithm. In: 2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 379–384Google Scholar
  30. 30.
    Kumar SR, Ganapathy S (2014) Artificial cooperative search algorithm based load frequency control of deregulated power system with SMES unit. J Theor Appl Inf Technol 63(1):20–29Google Scholar
  31. 31.
    Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332CrossRefGoogle Scholar
  32. 32.
    Elsisi M, Soliman M, Aboelela MAS, Mansour W (2016) Bat inspired algorithm based optimal design of model predictive load frequency control. Int J Electr Power Energy Syst 83:426–433CrossRefGoogle Scholar
  33. 33.
    Ghoshal SP (2004) Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr Power Syst Res 72(3):203–212CrossRefGoogle Scholar
  34. 34.
    Elsisi M, Soliman M, Aboelela MAS, Mansour W (2017) Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid. IET Gener Transm Distrib 11(16):3974–3983CrossRefGoogle Scholar
  35. 35.
    Ardalan Z, Karimi S, Poursabzi O, Naderi B (2015) A novel imperialist competitive algorithm for generalized traveling salesman problems. Appl Soft Comput 26:546–555CrossRefGoogle Scholar
  36. 36.
    Ogata K (2001) Modern control engineering. Prenctice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  37. 37.
    Kumar A, Kumar A, Chanana S (2010) Genetic fuzzy PID controller based on adaptive gain scheduling for load frequency control. In: 2010 Joint international conference on power electronics, drives and energy systems (PEDES) & 2010 power India. IEEE, pp 1–8Google Scholar
  38. 38.
    Selvakumaran S, Rajasekaran V, Karthigaivel R (2014) Genetic algorithm tuned IP controller for Load Frequency Control of interconnected power systems with HVDC links. Arch Electr Eng 63(2):161–175CrossRefGoogle Scholar
  39. 39.
    Dwivedi A, Ray G, Sharma AK (2016) Genetic algorithm based decentralized PI type controller: load frequency control. J Inst Eng (India) Ser B 97(4):509–515CrossRefGoogle Scholar
  40. 40.
    Soheilirad M, Karami K, Othman ML, Farzan P (2013) PID controller adjustment for MA-LFC by using a hybrid Genetic-Tabu Search Algorithm. In: System engineering and technology (ICSET), 2013 IEEE 3rd international conference on IEEE 2013, pp 197–202Google Scholar
  41. 41.
    Mahto T, Mukherjee V (2015) Frequency stabilisation of a hybrid two-area power system by a novel quasi-oppositional harmony search algorithm. Proc IET Gener Transm Distrib 9(15):2167–2179CrossRefGoogle Scholar
  42. 42.
    Ghoshal SP, Roy R (2008) Evolutionary computation based comparative study of TCPS and CES control applied to automatic generation control. In: Power system technology and IEEE power India conference, 2008. POWERCON 2008Google Scholar
  43. 43.
    Atashpaz-Gargari E, Lucas C (2007) Designing an optimal PID controller using Imperialist Competitive Algorithm. In: First joint congress on fuzzy and intelligent systems. Ferdowsi University of Mashhad, pp 29–31Google Scholar
  44. 44.
    Atashpaz-Gargari, E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007, pp 4661–4667Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical Power and Machines Department, Faculty of Engineering (Shoubra)Benha UniversityCairoEgypt

Personalised recommendations