Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

Abstract

This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

Keywords

Cuckoo search algorithm force control genetic algorithm hydraulic servo system model predictive control

Get full access to this article

View all access options for this article.

References

Morari

Lee

JH.

Model predictive control: past, present and future. Comput Chem Eng 1999; 23(4–5): 667–682.

Van der Lee

Svrcek

Young

BR.

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making. ISA T 2008; 47(1): 53–59.

Al-Duwaish

Naeem

. Nonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms. In: Proceedings of the 2001 IEEE international conference control applications (CCA’01), Mexico City, Mexico, 7 September 2001, pp.465–469. New York: IEEE.

Geyer

Quevedo

DE.

Performance of multistep finite control set model predictive control for power electronics. IEEE T Power Electr 2015; 30(3): 1633–1644.

Bououden

Chadli

Karimi

HR.

A robust predictive control design for nonlinear active suspension systems. Asian J Control 2016; 18(1): 122–132.

Nikdel

Badamchizadeh

, et al. Using neural network model predictive control for controlling shape memory alloy-based manipulator. IEEE T Ind Electron 2014; 61(3): 1394–1401.

Peng

Wang

Wei

Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization. J Zhejiang Univ: Sci C 2014; 15(2): 139–146.

Udugama

Wolfenstetter

Kirkpatrick

, et al. A comparison of a novel robust decentralised control strategy and MPC for industrial high purity, high recovery, multicomponent distillation. ISA T 2017; 69: 222–233.

Afram

Janabi-Sharifi

Theory and applications of HVAC control systems: a review of model predictive control (MPC). Build Environ 2014; 72: 343–355.

10.

Yuan

H-B

H-C

Kim

Y-B.

System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control. J Vib Control 2017; 24: 4145–4159.

11.

Essa

MESM

Aboelela

MAS

Moustafa Hassan

. Position control of hydraulic servo system using proportional-integral-derivative controller tuned by some evolutionary techniques. J Vib Control 2016; 22(12): 2946–2957.

12.

Aboelela

MAS

Essa

MESM

Moustafa Hassan

. Modeling and identification of hydraulic servo systems. Int J Model Simul 2018; 38: 139–149.

13.

Nwulu

Xia

Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs. Energy 2015; 91: 404–419.

14.

Bone

Xue

Flett

Position control of hybrid pneumatic-electric actuators using discrete-valued model-predictive control. Mechatronics 2015; 25: 1–10.

15.

Šitum

. Force and position control of a hydraulic press [VENTIL-revija za fluidno tehniko, avtomatizacijo in mehatroniko]. J Fluid Power, Automat Mech 2011; 17(4): 314–320.

16.

Wos

Dindorf

Practical parallel position-force controller for electro-hydraulic servo drive using on-line identification. Int J Dynam Control 2016; 4(1): 52–58.

17.

Gebrewold

Jungong

. Modeling and simulation on fuzzy-PID position controller of electro hydraulic servo system. Int J Sci Res 2015; 4(6): 1000–1005.

18.

Tri

Nam

DNC

Park

, et al. Trajectory control of an electro hydraulic actuator using an iterative back stepping control scheme. Mechatronics 2015; 29: 96–102.

19.

Nourizadeh

Rezaei

Zareinejad

, et al. Robust hydraulic actuator force control through relief discharge. Proc IMechE, Part I: J Systems and Control Engineering 2015; 229: 308–318.

20.

Yaghoobi

Mojallali

Tuning of a PID controller using improved chaotic Krill Herd algorithm. Optik 2016; 127(11): 4803–4807.

21.

Khadanga

Padhy

Panda

, et al. Design and analysis of multi-stage PID controller for frequency control in an islanded micro-grid using a novel hybrid whale optimization-pattern search algorithm. Int J Numer Model El 2018; 31(5): e2349.

22.

Bhatt

Parmar

Gupta

Whale optimized PID controllers for LFC of two area interconnected thermal power plants. ICTACT J Microelectr 2018; 3(4): 467–472.

23.

Essa

MESM

Aboelela

Moustafa Hassan

. Application of fractional order controllers on experimental and simulation model of hydraulic servo system. In: Azar

Vaidyanathan

Ouannas

(eds) Fractional order control and synchronization of chaotic systems. Cham: Springer, 2017, pp.277–324.

24.

Ljung

. MATLAB and system identification toolbox release 2015 (Software). Natick, MA: The MathWorks Inc, 2014.

25.

Essa

MESM

Aboelela

Moustafa Hassan

, et al. Model predictive force control of hardware implementation for electro-hydraulic servo system. T I Meas Control 2018; 41: 1435–1446.

26.

Abdelaziz

Ali

ES.

Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. Int J Elec Power 2015; 73: 632–643.

27.

Yang

Deb

Cuckoo search via Lévy flights. In: Proceedings of the 2009 world congress on nature & biologically inspired computing (NaBIC), Coimbatore, India, 9–11 December 2009, pp.210–214. New York: IEEE.

28.

Mandelbrot

. The fractal geometry of nature, vol. 173. New York: WH freeman, 1983.

29.

Gen

Cheng

Genetic algorithms and engineering optimization, vol. 7. Hoboken, NJ: John Wiley & Sons, 2000.

30.

Holland

. Adaptation in natural and artificial systems: an introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press, 1975.

31.

Goldberg

DE.

Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989.

32.

Fogarty

. An incremental genetic algorithm for real-time optimisation. In: Conference Proceedings, IEEE international conference on systems, man and cybernetics, Cambridge, MA, 14–17 November 1989, pp.321–326. New York: IEEE.

33.

Tammam

Aboelela

MAS

Moustafa Hassan

, et al. A multi-objective genetic algorithm based PID controller for load frequency control of power systems. Int J Em Tech Adv Eng 2013; 3(12): 463–467.

34.

Dotoli

Fay

Miśkowicz

, et al. Advanced control in factory automation: a survey. Int J Prod Res 2017; 55(5): 1243–1259.

35.

Dubljevic

Linear model predictive control for transport-reaction processes. AIChE J 2017; 63(7): 2644–2659.

36.

Kumar

Ramakrishna

Haq

SS.

A non-linear model predictive control (NMPC) energy management control strategy for standalone DC microgrids. Int J Core Eng Manag 2017: 135–147, http://ijcem.in/wp-content/uploads/2017/04/A-NON-LINEAR-MODEL-PREDICTIVE-CONTROL-NMPC-ENERGY-MANAGEMENT-CONTROL-STRATEGY-FOR-STANDALONE-DC-MICROGRIDS-NCIAEE-0181.pdf

37.

Essa

MESM

Aboelela

Hassan

, et al. Hardware in the loop of position tracking control of hydraulic servo mechanism. In: Proceedings of the 2017 13th international computer engineering conference (ICENCO), Cairo, Egypt, 27–28 December 2017, pp.160–165. New York: IEEE.