Inner Loop Control Systems Design for a Small Fixed-Wing UAV Using Metaheuristics

Authors

  • Nattapong Ruenruedeepan นักศึกษา หลักสูตรวิศวกรรมศาสตรมหาบัณฑิต สาขาวิชาวิศวกรรมเครื่องกล คณะวิศวกรรมศาสตร์ มหาวิทยาลัยขอนแก่น
  • Yodsadej Kanokmedhakul นักศึกษา หลักสูตรวิศวกรรมศาสตรดุษฎีบัณฑิต สาขาวิชาวิศวกรรมเครื่องกล คณะวิศวกรรมศาสตร์ มหาวิทยาลัยขอนแก่น
  • Nantiwat Pholdee รองศาสตราจารย์ สาขาวิชาวิศวกรรมเครื่องกล คณะวิศวกรรมศาสตร์ มหาวิทยาลัยขอนแก่น
  • Sujin Bureerat ศาสตราจารย์ สาขาวิชาวิศวกรรมเครื่องกล คณะวิศวกรรมศาสตร์ มหาวิทยาลัยขอนแก่น

Keywords:

PID controller, Metaheuristic, Unmanned aerial vehicle flight control system

Abstract

This paper presents optimisation design of inner loop control systems design of a small fixed-wing UAV using metaheuristics. The control system is divided into longitudinal and lateral control sub-systems which are decoupled for simplify. The flight control optimisation problem is posted to find control gains in the control system in order to minimizing settling time, steady state error, and control effort. Six metaheuristic optimisers are used to solve the proposed problem and their performance are statistical investigated. The results demonstrate that the LSHADE algorithm proficiency is dominant over the other algorithms for the proposed optimisation design problem of inner loop control systems.

References

Prisacariu V. THE HISTORY AND THE EVOLUTION OF UAVs FROM THE BEGINNING TILL THE 70s. 8(1):9.

FARI S. Guidance and Control for a Fixed-wing UAV [Internet]. [Italy]: Politecnico di Milano; 2017. Available from: https://www.politesi.polimi.it/handle/10589/137455

Beard RW, McLain TW. Small unmanned aircraft: Theory and practice. 2012. (Small Unmanned Aircraft: Theory and Practice).

EA A. my phd thesis with the title of (autonomous flight control system (autopilot) design using embedded systems). 2016 [cited 2021 May 25]; Available from: http://rgdoi.net/10.13140/RG.2.1.5124.2481

Korkmaz H, Erti̇N OB, Kasnakoğlu C, Kaynak ünver. Design of a Flight Stabilizer System for a Small Fixed Wing Unmanned Aerial Vehicle using System Identification. IFAC Proceedings Volumes. 2013;46(25):145–149.

Elkaim GH, Lie FAP, Gebre-Egziabher D. Principles of Guidance, Navigation, and Control of UAVs. Valavanis KP, Vachtsevanos GJ, editors. 2012;

Manocha A, Sharma A. Three Axis Aircraft Autopilot Control Using Genetic Algorithms : An Experimental Study. In: 2009 IEEE International Advance Computing Conference [Internet]. Patiala, India: IEEE; 2009 [cited 2021 Jul 27]. p. 171–174. Available from: http://ieeexplore.ieee.org/document/4809001/

Abachizadeh M, Yazdi MRH, Yousefi-Koma A. Optimal tuning of PID controllers using Artificial Bee Colony algorithm. In: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics [Internet]. Montreal, QC, Canada: IEEE; 2010 [cited 2021 Jul 27]. p. 379–384. Available from: http://ieeexplore.ieee.org/document/5695861/

Ashari A. Flight Trajectory Control System on Fixed Wing UAV using Linear Quadratic Regulator. International Journal of Engineering Research and. 2019 Aug 22;V8.

Kulcsár B. LQG/LTR CONTROLLER DESIGN FOR AN AIRCRAFT MODEL. Periodica Polytechnica Transportation Engineering. 2000;28(1–2):131–142.

McLean D, Matsuda H. Helicopter station-keeping: comparing LQR, fuzzy-logic and neural-net controllers. Engineering Applications of Artificial Intelligence. 1998;11(3):411–418.

Wahid N, Rahmat MF. Pitch control system using LQR and Fuzzy Logic Controller. In: 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA). 2010. p. 389–394.

Usta MA, Akyazi Ö, Akpinar AS. Aircraft roll control system using LQR and fuzzy logic controller. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications. 2011. p. 223–237.

Ho WK, Gan OP, Tay EB, Ang EL. Performance and gain and phase margins of well-known PID tuning formulas. IEEE Trans Contr Syst Technol. 1996 Jul;4(4):473–477.

Vishal, Ohri J. GA tuned LQR and PID controller for aircraft pitch control. In: 2014 IEEE 6th India International Conference on Power Electronics (IICPE) [Internet]. Kurukshetra, India: IEEE; 2014 [cited 2021 Jul 27]. p. 1–6. Available from: http://ieeexplore.ieee.org/document/7115839/

Bender D, Laub A. The linear-quadratic optimal regulator for descriptor systems. In: 1985 24th IEEE Conference on Decision and Control [Internet]. Fort Lauderdale, FL, USA: IEEE; 1985 [cited 2021 Jul 27]. p. 957–962. Available from: http://ieeexplore.ieee.org/document/4048442/

Aliyu B, Chindo A, Opasina A, Abdulrahaman A. Comparative Design for Improved LQG Control of Longitudinal Flight Dynamics of a Fixed-Wing UAV. AIR. 2015 Jan 10;3(5):477–487.

Ferreira HC, Baptista RS, Ishihara JY, Borges GA. Disturbance rejection in a fixed wing UAV using nonlinear H∞ state feedback. In: 2011 9th IEEE International Conference on Control and Automation (ICCA) [Internet]. Santiago, Chile: IEEE; 2011 [cited 2021 Jul 27]. p. 386–391. Available from: http://ieeexplore.ieee.org/document/6138036/

Ghambari S, Lepagnot J, Jourdan L, Idoumghar L. A comparative study of meta-heuristic algorithms for solving UAV path planning. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI) [Internet]. Bangalore, India: IEEE; 2018 [cited 2021 Jul 24]. p. 174–181. Available from: https://ieeexplore.ieee.org/document/8628807/

Kaveh A, Bakhshpoori T. An efficient multi-objective cuckoo search algorithm for design optimization. Advances in Computational Design. 2016 Jan 25;1(1):87–103.

Kaveh A, Farhoudi N. Dolphin Echolocation Optimization: Continuous search space. Advances in Computational Design. 2016 Apr 25;1(2):175–194.

Kaveh A, Ilchi Ghazaan M. Truss optimization with dynamic constraints using UECBO. Advances in Computational Design. 2016 Apr 25;1(2):119–138.

Mittal N, Garg A, Singh P, Singh S, Singh H. Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties. Nat Comput [Internet]. 2020 Nov 10 [cited 2021 Jul 24]; Available from: http://link.springer.com/10.1007/s11047-020-09811-5

Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. 2020 Mar;191:105190.

Hansen N, Müller SD, Koumoutsakos P. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation. 2003 Mar;11(1):1–18.

Jingqiao Z, Sanderson AC. JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Trans Evol Computat. 2009 Oct;13(5):945–958.

Viktorin A, Pluhacek M, Senkerik R. Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. In: 2016 IEEE Congress on Evolutionary Computation (CEC) [Internet]. Vancouver, BC, Canada: IEEE; 2016 [cited 2021 Jul 24]. p. 4797–4803. Available from: http://ieeexplore.ieee.org/document/7744404/

Tanabe R, Fukunaga AS. Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC) [Internet]. Beijing, China: IEEE; 2014 [cited 2021 Feb 10]. p. 1658–1665. Available from: https://ieeexplore.ieee.org/document/6900380

Bravo-Mosquera PD, Botero-Bolivar L, Acevedo-Giraldo D, Cerón-Muñoz HD. Aerodynamic design analysis of a UAV for superficial research of volcanic environments. Aerospace Science and Technology. 2017 Nov;70:600–614.

ÇET˙IN E. SYSTEM IDENTIFICATION AND CONTROL OF A FIXED WING AIRCRAFT BY USING FLIGHT DATA OBTAINED FROM X-PLANE FLIGHT SIMULATOR. MIDDLE EAST TECHNICAL; 2018.

Sörensen K, Sevaux M, Glover F. A History of Metaheuristics. In: Martí R, Pardalos PM, Resende MGC, editors. Handbook of Heuristics [Internet]. Cham: Springer International Publishing; 2018 [cited 2021 Feb 10]. p. 791–808. Available from: http://link.springer.com/10.1007/978-3-319-07124-4_4

Whitley D. A genetic algorithm tutorial. Stat Comput [Internet]. 1994 Jun [cited 2021 May 26];4(2). Available from: http://link.springer.com/10.1007/BF00175354

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Published

2022-08-06

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บทความวิจัย