A model-based approach for jet aircraft lateral motion control with constraints satisfaction

Authors

  • Zeeshan Rashid Department of Electrical Engineering, The Islamia University of Bahawalpur
  • Shadi Khan Baloch Department of Electrical Engineering, Institute of Business Management (IoBM), Karachi Pakistan
  • M. Mohsin Siraj Department of Electrical Engineering, Eindhoven University of Technology (TU/e), The Netherlands
  • Ghulam Amjad Hussain Department of Electrical Engineering, College of Arts and Sciences. American University of Kuwait, Safat, Kuwait

Abstract

Design of a potentially robust autopilot to control lateral motion of a jet aircraft for maintaining the balance, turbulence rejection, handling asymmetric wind pressure, linearization and constraints on the inputs imposes technological and computational challenges for certain control algorithms. Especially, when the multiple states and inputs are strongly coupled to each other, it is imperative to evaluate the performance of most efficient control schemes which not only provide stable and error free response but also fulfill the system requirements with minimum computational cost. This paper demonstrates lateral motion control of a jet aircraft using state feedback controllers, proportional integral derivative controller and model predictive controller to evaluate and compare the control objectives. In a block diagram framework as a function of elementary tuning parameters, all strategies are implemented on a linearized state space model which is furnished by the set of fundamental equations of motion. The effects of disturbance, input and output constraints, sampling time and different controller gains are studied for the underlying multiple input multiple output system. State feedback algorithms provide minimum flexibility to achieve the control objectives in restraining the output within constraint boundaries. Proportional integral derivative controller is more flexible, yet not able to impose the limitation on both the input/output pair. Finally, model predictive controller presents the most efficient features by virtue of response time, robustness, stability, cost and constraints fulfillment with minimal computation and input cost.

Author Biographies

Zeeshan Rashid, Department of Electrical Engineering, The Islamia University of Bahawalpur

Zeeshan Rashid received the Ph.D. degree from Koc¸ University, Istanbul, Turkey in 2018. Currently, he is working as Assistant Professor in Electrical Engineering Department, The Islamia University of Bahawalpur, Pakistan. His research interests include modeling of fiber lasers, harmonic wave propagation in smart grids, high frequency distortion in underground cables, model predictive control and modeling of low voltage power circuits at harmonic frequencies in a smart network.

Shadi Khan Baloch, Department of Electrical Engineering, Institute of Business Management (IoBM), Karachi Pakistan

Shadi Khan Baloch obtained his PhD in Electrical and Electronics Engineering from Koc University Istanbul Turkey in July 2013. He did his Bachelors in Electronics Engineering from Mehran UET Jamshoro Sindh Pakistan in 2008. His research interests include Micro-Electro-Mechanical Systems (MEMS) and Optofluidics (Refractive index based sensing) for sensing applications (Chemical and biosensors). He worked on European Union´s Horizon 2020 research and innovation program under grant agreement No 685648 project. He has five years of teaching/research assistant experience. He also worked as Assistant Manager Transmission in PTCL from 2009 to 2013.

M. Mohsin Siraj, Department of Electrical Engineering, Eindhoven University of Technology (TU/e), The Netherlands

M. Mohsin Siraj is a guest researcher at department of Electrical engineering, Eindhoven university of technology, The Netherlands.  Previously, he was a Postdoctoral and PhD degree student in the same department. Siraj's research interests include model-based control and optimization, handling uncertainty, robust optimization, risk management and data analytics (machine learning). He holds a bachelor's degree in Electronics and a master's degree in Systems and control.

Ghulam Amjad Hussain, Department of Electrical Engineering, College of Arts and Sciences. American University of Kuwait, Safat, Kuwait

Dr. Amjad Hussain is an Assistant Professor at AUK, Kuwait where he is involved in teaching Electrical Engineering courses and supervising research. Previously he has worked as a Senior R&D Engineer-Power Systems Technologies at EATON European Innovation Centre (EEIC), Prague, the Czech Republic. At EATON, where he was involved in state-of-the-art power system projects (R&D), involving advanced protection and control techniques and related commercial products. He has also worked as a Project Engineer from 2008 to 2010 with a major switchgear company in Gulf region and supervised construction and pre-commissioning of MV electrical distribution substations. He received the bachelor's degree in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2007, and the master's and the Ph.D. from Aalto University, School of Electrical Engineering, Finland, in 2012 and 2016 respectively. He has authored/; co-authored more than 30 articles in international journals and conferences and has one patent under review. He is the recipient of awards: The Honorable Mention Paper Prize at IEEE-IAS PCIC Conference 2014, San Francisco and The Best-evaluated Author at IEEE RTUCON 2016, Riga. He received multiple national research grants during doctoral research, total amount €80,000. His research expertise and interests include advanced power system protection, control and reconfiguration techniques in modern smart grids involving Distributed Energy Resources (DERs) and AC/DC hybrid microgrids, power quality, electrical fault detection and location, pre-emptive protection techniques, condition monitoring of power equipment, insulation diagnostic systems, and partial discharge (PD) measurements in medium voltage and high voltage equipment. He is a reviewer of various periodicals and conferences.

References

[1] Jonckheere E. A., Yu G. R., and Chien. C. C., “Gain scheduling for lateral motion of propulsion controlled aircraft using neural networks”, Proceedings of the American Control Conference, Vol. 5, pp. 3321–3325, USA, 1997.

[2] Chen X. H., Haq E. and Lin. J., “Design, modeling and tuning of modified PID controller for autopilot in MAVs”, IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 475–480, China, 2016.

[3] N.V.G., D.M.V. and George V., “Aircraft yaw control system using LQR and fuzzy logic controller”, International Journal of Computer Applications, Vol. 45, No. 9, pp. 25–30, 2012.

[4] Raffo G.V., Ortega M. G. and Rubio F.R.,“Back stepping/nonlinear H∞ control for path tracking of a quad rotor unmanned aerial vehicle”, American Control Conference, pp. 3356–3361, USA, 2008.

[5] Giacomo C., “Modeling, simulation and flight test for automatic flight control of the condor hybrid-electric remote piloted aircraft”, Ph.D. diss., Air Force Institute of Technology, USA, 2012.

[6] Lungu M., and Lungu R., “Automatic control of aircraft lateral-directional motion during landing using neural networks
and radio-technical subsystems”, Neurocomputing, Vol. 171, pp. 471–481, 2016.

[7] Lu L. K. and Turkoglu K., “Adaptive differential thrust methodology for lateral/directional stability of aircraft with a completely damaged vertical stabilizer”, International Journal of Aerospace Engineering, Vol. 2018, pp. 1 – 19, 2018.

[8] Ahmed W., Li Z., Maqsood H. and Anwar B., “System modeling and controller design for lateral and longitudinal motion of F-16”, Automation Control and Intelligent Systems, Vol. 4, No. 1, pp. 39 – 45, 2019.

[9] Kautsky J., Nichols N.K. and Dooren P.V., “Robust pole assignment in linear state feedback”, International Journal of Control, Vol. 41, No. 05, pp. 1129 – 1155, 1985.

[10] Wonham W. M., “On pole assignment in multi-input controllable linear systems”, IEEE Transactions on Automatic Control, Vol. 12, No. 06, pp. 660 – 665, 1967.

[11] Broussard J. R.,“A quadratic weight selection algorithm”, IEEE Transactions on Automatic Control, Vol. 27, No. 04, pp.
945 – 947, 1982.

[12] D. Ali, L. Hend, and M. Hassani, “Optimized eigen structure assignment by ant system and LQR approaches”, International Journal of Computer Science and Applications, Vol. 5, No. 04, pp.
45 – 56, 2008.

[13] Raemaekers A. J. M., “Design of a model predictive controller to control UAVs”, Technische Universiteit Eindhoven,
Vol. 2007.141, 2007.

[14] Ru P. and Subbarao K., “Non linear model predictive control for unmanned aerial vehicles”, Vol. 4, No. 31, pp. 1 – 26, 2017.

[15] Misra G., and Bai X., “Output feedback stochastic model predictive control for glideslope tracking during aircraft carrier landing”, Journal of Guidance, Control and Dynamics, Vol. 42, No. 9, pp. 2098 – 2105, 2019.

[16] McLean D., “Automatic flight control systems”, Prentice Hall International, 1990.

[17] Lin P. N. W., Kham N. L. and Tun H. M., “Longitudinal and lateral dynamic system modeling of a fixed wing UAV”, International Journal of Scientific and Technology Research, Vol. 6, No. 4, pp. 171 – 174, 2017.
[18] McClean D, “Automatic flight control systems”, Prentice Hall International Series in Systems and Control Engineering, 1990.

[19] Chvojka M., “Dynamic characteristics of an airplane”, 2004.

[20] Barsaiyan P. and Purwar S., “Comparison of state feedback controller design methods for MIMO systems”, International Conference on Power, Control and Embedded Systems, pp. 1 – 6, India. 2013.

[21] Raczynski D, Stanisławski W., “Controllability and observability gramians parallel computation using GPU”, Journal of Theoretical and Applied Computer Science, Vol. 6, No. 01, pp. 47 – 66, 2012.

[22] Purnawan H., Mardlijah and Purwanto E. B., “Design of linear quadratic regulator (lqr) control system for flight stability of LSU-05”, Journal of Physics: Conference Series, Vol. 890, No. 01, pp. 012056, 2017.

[23] Etkin B., Reid L. D., “Dynamics of flight: stability and control”, John Wiley & Sons, Inc., 3rd ed.,1959.

[24] Rawlings J. B. and David M., “Model predictive control: theory and design”, Nob Hill Publishing, 2009.

[25] Maciejowski J. M., “Predictive control: with constraints”, Pearson Education, 2002.

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Published

2021-01-19

Issue

VOLUME 28 JANUARY 2021

Section

Electrical Engineering