Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance

Saadat, Seyyed Amirhossein; Fateh, Mohammad Mehdi; keighobadi, javad

doi:10.55730/1300-0632.4059

Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance

Seyyed Amirhossein SAADAT, (Shahrood University of Technology, Shahrood, Semnan, Iran)

Mohammad Mehdi FATEH, (Shahrood University of Technology, Shahrood, Semnan, Iran)

Javad KEIGHOBADI (Shahrood University of Technology, Shahrood, Semnan, Iran)

Turkish Journal of Electrical Engineering and Computer Sciences

39 0

Yıl: 2024 Cilt: 32 Sayı: 1 Sayfa Aralığı: 126 - 143 Metin Dili: İngilizce DOI: 10.55730/1300-0632.4059 İndeks Tarihi: 14-03-2024

Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance

Öz:

In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is eﬀicient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown terms, providing information for the disturbance observer. Besides, the tuning parameters are chosen optimally by adopting the grey wolf optimization (GWO) algorithm. The closed-loop system stability is also proven by the barrier Lyapunov function (BLF) while the total uncertainties, including system dynamics, friction, and disturbance, are tracked by the total estimation. Thus, the predetermined performance, robust behavior, and high-precision estimation are the achievements of the presented controller for PTS. To confirm the ability of the proposed control idea, simulations are provided. Furthermore, a comparison scenario is also considered to emphasize the supremacy of the proposed control system.

Anahtar Kelime: Neural network passive torque simulator state constraint disturbance observer grey wolf optimization algorithm robust control.

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Li C, Pan X, Wang G. Torque tracking control of electric load simulator with active motion disturbance and nonlinearity based on T-S fuzzy model. Asian Journal of Control 2020; 22 (3): 1280–1294.
[2] Shamisa A, Kiani Z. Robust fault-tolerant controller design for aerodynamic load simulator. Aerospace Science and Technology 2018; 78: 332–341.
[3] Koh DW, Park SY, Kim DH, Choi KH. Development of a hardware-in-the-loop simulator for spacecraft attitude control using thrusters. Journal of Astronomy and Space Sciences 2009; 26 (1): 47–58.
[4] Jeon SW, Jung S. Hardware-in-the-loop simulation for the reaction control system using PWM-based limit cycle analysis. IEEE Transactions on control systems technology 2011; 20 (2): 538–545.
[5] Li H, Steurer M, Shi K, Woodruff S, Zhang D. Development of a unified design, test, and research platform for wind energy systems based on hardware-in-the-loop real-time simulation. IEEE Transactions on Industrial Electronics
2006; 53 (4): 1144–1151. [6] Crăciun O, Florescu A, Munteanu I, Bratcu AI, Bacha S et al. Hardware-in-the-loop simulation applied to protection devices testing. International Journal of Electrical Power and Energy Systems 2014; 54: 55–64.
[7] Lee M, Lee H, Lee KS, et al. Development of a hardware in the loop simulation system for electric power steering in vehicles.International journal of Automotive technology 2011; 12 (5): 733.
[8] Palladino A, Fiengo G, Lanzo D. A portable hardware-in-the-loop (HIL) device for automotive diagnostic control systems. ISA Transactions 2012; 51 (1): 229–236.
[9] Hwang A, Yoon S, Kim TY, Kwon DY, Choi C et al. Verification of unmanned underwater vehicle with velocity over 10 knots guidance control system based on hardware in the loop simulation. In: IEEE.; 2009: 1–5.
[10] Martin A, Emami MR. Dynamic load emulation in hardware-in-the-loop simulation of robot manipulators. IEEE Transactions on Industrial Electronics 2010; 58 (7): 2980–2987.
[11] Chhabra R, Emami MR. A holistic concurrent design approach to robotics using hardware-in-the-loop simulation. Mechatronics 2013; 23 (3): 335–345.
[12] Burbank JL, Kasch W, Ward J. An introduction to network modeling and simulation for the practicing engineer. John Wiley & Sons . 2011.
[13] Gawthrop P, Virden D, Neild S, Wagg D. Emulator-based control for actuator-based hardware-in-the-loop test- ing.Control Engineering Practice 2008; 16 (8): 897–908.
[14] Ullah N, Wang SP. Backstepping Control of Electrical Load Simulator with Adaptive Tracking Performance Con- troller. In: . 245. Trans Tech Publ. ; 2013: 310–315.
[15] Yang B, Liu F, Zhang M. A loading control strategy for electric load simulator based on new mapping approach and fuzzy inference in Cerebellar Model Articulation Controller. Measurement and Control 2019; 52 (1-2): 131–144.
[16] Fu Z, Wang S, Wang X. ESO-based adaptive robust force control of linear electric load simulator. 2018.
[17] Li Z, Shang Y, Jiao Z, Wu S, Yao J. Surplus Torque elimination control of electro-hydraulic load simulator based on actuator velocity input feedforward compensating method. Journal of Dynamic Systems, Measurement, and Control 2018; 140 (10).
[18] Li C, Li Y, Wang G. H∞ output tracking control of electric-motor-driven aerodynamic load simulator with external active motion disturbance and nonlinearity. Aerospace Science and Technology 2018; 82: 334–349.
[19] Tian W, Pan W, Shao Y, Xu B, Liu H et al. Individual pitch control strategy for reducing aerodynamic loads and torque ripples. IEEJ Transactions on Electrical and Electronic Engineering 2019; 14 (11):1624–1632.
[20] Jing C, Xu H, Jiang J. Dynamic surface disturbance rejection control for electro-hydraulic load simulator. Mechanical Systems and Signal Processing 2019; 134: 106293.
[21] Borges FAP, Perondi EA, Cunha MAB, Sobczyk MR. A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2021; 43 (5): 1–19.
[22] Zhang B, Li C, Wang T, Wang Z, Ma H. Design and experimental study of zero-compensation steering gear load simulator with double torsion springs. Measurement 2019; 148: 106930.
[23] Jing C, Xu H, Jiang J. Practical torque tracking control of electro-hydraulic load simulator using singular pertur- bation theory. ISA Transactions 2020; 102: 304–313.
[24] Dai M, Qi R, Zhao Y, Li Y. PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator. Electronics 2021;10 (7): 811.
[25] Saadat SA, Fateh MM, Keighobadi J. Adaptive state augmented clustering-based fuzzy learning control of a passive torque simulator. International Journal of Dynamics and Control 2021: 1–13. [26] Keighobadi J, Fateh MM, Xu B. Adaptive fuzzy voltage-based backstepping tracking control for uncertain robotic manipulators subject to partial state constraints and input delay. Nonlinear Dynamics 2020; 100 (3): 2609–2634.
[27] Fang Y, Kong H, Ding D. Novel fast terminal sliding mode controller with current constraint for permanent-magnet synchronous motor. Turkish Journal of Electrical Engineering and Computer Sciences 2021; 29 (3): 1821–1835.
[28] Li Y, Liu Z, Wang Z, Yin Y, Zhao B. Adaptive control of teleoperation systems with prescribed tracking performance: a BLF-based approach. International Journal of Control 2022; 95 (6): 1600–1610.
[29] Yang Z, Dong C, Zhang X, Wang G. Full-state time-varying asymmetric constraint control for non-strict feedback nonlinear systems based on dynamic surface method. Scientific Reports 2022; 12 (1): 10469.
[30] Wei Y, Wang H, Tian Y. Asymmetric time varying BLF based model free hybrid force/position control for SEA based 2 DOF manipulator. International Journal of Adaptive Control and Signal Processing. 2023; 37 (7): 1716–1737.
[31] R.-C. Roman, R.-E. Precup, E. M. Petriu, M. Muntyan. “Fictitious reference iterative tuning of discrete-time model-free control for tower crane systems,” STUDIES IN INFORMATICS AND CONTROL, vol. 32, no. 1, pp. 5–14, 2023.
[32] Saadat SA, Ghamari SM, Mollaee H. Adaptive backstepping controller design on Buck converter with a novel improved identification method. IET Control Theory & Applications 2022; 16 (5): 485–495.
[33] Chi R, Li H, Shen D, Hou Z, Huang B. “Enhanced p-type control: Indirect adaptive learning from set-point updates,” IEEE Transactions on Automatic Control, vol. 68, no. 3, pp. 1600–1613, 2022.
[34] Wang X, Wang S, Yao B. Adaptive robust control of linear electrical loading system with dynamic friction com- pensation. In: IEEE.; 2010: 908–913.
[35] De Wit CC, Olsson H, Astrom KJ, Lischinsky P. A new model for control of systems with friction. IEEE Transactions on Automatic Control 1995; 40 (3): 419–425.
[36] Broomhead DS, Lowe D. Radial basis functions, multi-variable functional interpolation and adaptive networks. tech. rep., Royal Signals and Radar Establishment Malvern (United Kingdom); 1988.
[37] Awad M. Enhanced hybrid method of divide-and-conquer and RBF neural networks for function approximation of complex problems. Turkish Journal of Electrical Engineering and Computer Sciences 2017; 25 (2): 1095–1105.
[38] Xu B, Sun F. Composite intelligent learning control of strict-feedback systems with disturbance. IEEE Transactions on Cybernetics 2018; 48 (2): 730–741.
[39] Ren B, Ge SS, Tee KP, Lee TH. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function. IEEE Transactions on Neural Networks 2010; 21 (8): 1339–1345.
[40] Keighobadi J, Fateh MM. Adaptive Robust Tracking Control Based on Backstepping Method for Uncertain Robotic Manipulators Including Motor Dynamics. International Journal of Industrial Electronics Control and Optimization 2021; 4 (1): 13–22.
[41] Deng H, Krstić M. Stochastic nonlinear stabilization—I: A backstepping design. Systems & Control Letters 1997; 32 (3): 143–150.
[42] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software 2014; 69: 46–61.
[43] Majeed MM, Patri SR. An enhanced grey wolf optimization algorithm with improved exploration ability for analog circuit design automation. Turkish Journal of Electrical Engineering and Computer Sciences 2018;26 (5): 2605–2617.
[44] Keighobadi J, Xu B, Alfi A, Arabkoohsar A, Nazmara G. Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics. ISA Transactions 2022; 131: 246–263.
[45] Ullah N, Wang S, Wang X. Fuzzy backstepping torque control of passive torque simulator with algebraic parameters adaptation. Journal of Electrical Engineering 2015; 66 (4): 203–213.
[46] Ullah N, Aziz-Al Ahmadi A. Non integer order modeling and control of aerodynamic load simulator system. IEEE Access 2019; 7: 160177–160190.
[47] Xingjian W, Shaoping W, Pan Z. Adaptive fuzzy torque control of passive torque servo systems based on small gain theorem and input-to-state stability. Chinese Journal of Aeronautics 2012; 25 (6): 906–916.
[48] Saadat SA, Fateh MM, Keighobadi J. Backstepping-based adaptive constrained control of passive torque simulator using function approximation technique. in 2022 30th International Conference on Electrical Engineering (ICEE). IEEE, 2022, pp.603–608.
[49] Chen WH, Ballance DJ, Gawthrop PJ, O’Reilly J. A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics 2000; 47 (4): 932-938.
[50] Ames AD, Xu X, Grizzle JW, Tabuada P. Control barrier function based quadratic programs for safety critical systems. IEEE Transactions on Automatic Control 2016; 62 (8): 3861-3876.

APA	Saadat S, Fateh M, keighobadi j (2024). Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. , 126 - 143. 10.55730/1300-0632.4059
Chicago	Saadat Seyyed Amirhossein,Fateh Mohammad Mehdi,keighobadi javad Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. (2024): 126 - 143. 10.55730/1300-0632.4059
MLA	Saadat Seyyed Amirhossein,Fateh Mohammad Mehdi,keighobadi javad Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. , 2024, ss.126 - 143. 10.55730/1300-0632.4059
AMA	Saadat S,Fateh M,keighobadi j Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. . 2024; 126 - 143. 10.55730/1300-0632.4059
Vancouver	Saadat S,Fateh M,keighobadi j Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. . 2024; 126 - 143. 10.55730/1300-0632.4059
IEEE	Saadat S,Fateh M,keighobadi j "Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance." , ss.126 - 143, 2024. 10.55730/1300-0632.4059
ISNAD	Saadat, Seyyed Amirhossein vd. "Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance". (2024), 126-143. https://doi.org/10.55730/1300-0632.4059

APA	Saadat S, Fateh M, keighobadi j (2024). Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences, 32(1), 126 - 143. 10.55730/1300-0632.4059
Chicago	Saadat Seyyed Amirhossein,Fateh Mohammad Mehdi,keighobadi javad Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences 32, no.1 (2024): 126 - 143. 10.55730/1300-0632.4059
MLA	Saadat Seyyed Amirhossein,Fateh Mohammad Mehdi,keighobadi javad Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences, vol.32, no.1, 2024, ss.126 - 143. 10.55730/1300-0632.4059
AMA	Saadat S,Fateh M,keighobadi j Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 126 - 143. 10.55730/1300-0632.4059
Vancouver	Saadat S,Fateh M,keighobadi j Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 126 - 143. 10.55730/1300-0632.4059
IEEE	Saadat S,Fateh M,keighobadi j "Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance." Turkish Journal of Electrical Engineering and Computer Sciences, 32, ss.126 - 143, 2024. 10.55730/1300-0632.4059
ISNAD	Saadat, Seyyed Amirhossein vd. "Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance". Turkish Journal of Electrical Engineering and Computer Sciences 32/1 (2024), 126-143. https://doi.org/10.55730/1300-0632.4059