Document Type : Original Article

Authors

Department of Faculty of Toosi University of Technology

Abstract

The paper compares the performance of two altitude controllers, model predictive controller (MPC) and linear quadratic requlator (LQR), for aircraft in cruise flight and height change conditions. The design of the controllers is based on the linearized state space matrix of the aircraft’s longitudinal motion around the trim conditions. The controllers’ ability to track the desired altitude while satisfying input and state constraints is evaluated, and it is found that both controllers are effective in maintaining the desired height. However, the MPC controller performs less overshoot, settling time and transient error than the LQR controller and achieves a more efficient control input by predicting the future behavior of the system. The proposed altitude controllers provide a promising solution for maintaining the desired aircraft altitude in cruise flight conditions, and the comparative analysis of the two control methods can assist in selecting the appropriate control strategy for a given aircraft system based on the desired performance requirements.

Keywords

Main Subjects

Article Title [Persian]

Design and Comparison of MPC and LQR Control Methods for a Passenger Aircraft

Authors [Persian]

  • Amirali Nikkhah
  • Moein Ebrahimi
  • Morteza Tayfi
  • Navid Mohammadi

Department of Faculty of Toosi University of Technology

Abstract [Persian]

The paper compares the performance of two altitude controllers, model predictive controller (MPC) and linear quadratic requlator (LQR), for aircraft in cruise flight and height change conditions. The design of the controllers is based on the linearized state space matrix of the aircraft’s longitudinal motion around the trim conditions. The controllers’ ability to track the desired altitude while satisfying input and state constraints is evaluated, and it is found that both controllers are effective in maintaining the desired height. However, the MPC controller performs less overshoot, settling time and transient error than the LQR controller and achieves a more efficient control input by predicting the future behavior of the system. The proposed altitude controllers provide a promising solution for maintaining the desired aircraft altitude in cruise flight conditions, and the comparative analysis of the two control methods can assist in selecting the appropriate control strategy for a given aircraft system based on the desired performance requirements.

Keywords [Persian]

  • MPC
  • LQR
  • Fixed wing
  • Aircraft
  • Altitude control
  • actuator saturation
  • Barmpounakis, E.N., E.I. Vlahogianni, and J.C. Golias, Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges.
  • Rahimi, M.R., S. Hajighasemi, and D. Sanaei, Designing, and simulation for vertical moving control of UAV system using PID, LQR, and Fuzzy Logic. International Journal of Electrical and Computer Engineering, 2013. 3(5): p. 651.
  • Turkoglu, K., et al., PID parameter optimization of an UAV longitudinal flight control system. International Journal of Aerospace and Mechanical Engineering, 2008. 2(9): p. 1031-1036.
  • Kada, B. and Y. Ghazzawi. Robust PID controller design for an UAV flight control system. in Proceedings of the World congress on Engineering and Computer Science. 2011.
  • Balas, G.J., Flight control law design: An industry perspective. European Journal of Control, 2003. 9(2-3): p. 207-226.
  • Walker, G. and D. Allen. X-35B STOVL flight control law design and flying qualities. in 2002 Biennial International Powered Lift Conference and Exhibit. 2002.
  • Goupil, P., et al., AIRBUS efforts towards advanced real-time fault diagnosis and fault tolerant control. IFAC Proceedings Volumes, 2014. 47(3): p. 3471-3476.
  • Goupil, P. and A. Marcos, The European ADDSAFE project: Industrial and academic efforts towards advanced fault diagnosis. Control Engineering Practice, 2014. 31: p. 109-125.
  • Grondman, F., et al. Design and flight testing of incremental nonlinear dynamic inversion-based control laws for a passenger aircraft. in 2018 AIAA Guidance, Navigation, and Control Conference. 2018.
  • W Chowdhury, M., S. Keshmiri, and J. Xu. Design and Flight Test Validation of a UAS Lateral-directional Model Predictive Controller. in 2021 International Conference on Unmanned Aircraft Systems (ICUAS). 2021. IEEE.
  • Mammarella, M. and E. Capello. A robust MPC-based autopilot for mini UAVs. in 2018 International Conference on Unmanned Aircraft Systems (ICUAS). 2018. IEEE.
  • Dai, Y., et al., The lateral control during aircraft-on-ground deceleration phases. Aerospace Science and Technology, 2019. 95: p. 105482.
  • GHAHRAMANI, N., Naghash, A. and TOUHIDKHAH, F., 2008. Incremental Predictive Command of Velocity to Be Gained Guidance Method.
  • Ramezani, M. and Assadian, N., 2021. Model Predictive Fault Tolerant Control of Two-Tethered Satellite System. Journal of Aerospace Science and Technology, 14(1), pp.128-142.
  • van Ingen, J., C.C. de Visser, and D.M. Pool. Stall Model Identification of a Cessna Citation II from Flight Test Data Using Orthogonal Model Structure Selection. in AIAA Scitech 2021 Forum. 2021.
  • McLean, D., Automatic flight control systems(Book). Englewood Cliffs, NJ, Prentice Hall, 1990, 606, 199