Application and Evaluation of Laguerre Functions in Helicopter Flight Control System Designed by Model Predictive Control

Authors

Abstract

One of the major challenges in the use of predictive control systems, particularly in the fast system is computational burden. To accelerate the process and reduce the amount of computation and optimization of functions, we use the Laguerre functions. The purpose of this paper is to provide a linear predictive control method using Laguerre functions with constraints on signal and the rate control signal for a helicopter flight control system design in the status of hover maneuver. By tuning parameters of predictive control in nonlinear model, the performance of the proposed method evaluate  by two index entries, maneuverability and coupling effect based on standards for design a helicopter flight control system. Multiplicative uncertainty in state-space model has been described due to uncertainty on system modeling to study the robustness of the proposed method. The simulation results of this method have been compared with the results of generalized predictive control method. Simulation results show that in addition of public benefits in generalized predictive control, Laguerre predictive control has the advantage of reducing the time and burden of computation and it is clear to see that the control law we have obtained for the hovering flight condition achieve the top level performance in two categories under examination.

Keywords


  1. Luo, C.H., Liu, R.F., Yan, C.D., and Chang, Y.H. “Helicopter H∞ Control Design with Robust Flying Quality,” Aerospace Science and Technology, Vol. 7, No. 2, pp. 159–169, 2003.##
  2. Civita, M.L. “Integrated Modeling and Robust Control for Full Envelope Flight of Robotic Helicopters,” PhD thesis, Robotics Institute, Carnegie Mellon University, 2002.##
  3. Civita, M.L., Papageorgiou, G., Messner, W.C., and Kanade, T. “Design and Flight Testing of an H Controller for a Robotic Helicopter,” Journal of Guidance, Control and Dynamics, Vol. 29, No. 2, pp. 485-494, 2006.##
  4. Yaesh, I. and Shaked, U. “H-infinity Optimization with Pole Constraints of Static Output-Feedback Controllers - A Non-Smooth Optimization Approach,” IEEE Transaction Control Systems Technology, Vol. 20, No. 4, pp. 1066-1072, 2012.##
  5. Yuan, W. and Katupitiya, J. “Design of a μ synthesis controller to stabilize an unmanned helicopter,” 28th International Congress of the Aeronautical Sciences, 3317-3325, 2012.##
  6. Comasòlivas, R., Escobet, T., and Quevedo, J. “Automatic Design of Robust PID Rontrollers Based on QFT Specifications”; Proc. Int. Conf. Advances PID Control. Brescia, Italy, 2012.##
  7. Konstantin, K., Huck, S., and Summers, T. H. “Fast Model Predictive Control of Miniature Helicopters”; Euro. Conf. Control. Zurich, Switzerland, 2013.##
  8. Song, D., Han, J., and Liu, G. “Active Model-Based Predictive Control and Experimental Investigation on Unmanned Helicopters in Full Flight Envelope,” IEEE transactions on control systems technology, Vol. 4, pp.  1502-1509, 2013.##
  9. Budiyono, A. and Wibowo, S.S. “Optimal Tracking Controller Design for a Small Scale Helicopter,” Journal of Bionic Engineering, Vol. 4, No. 4, pp. 271−280, 2007.##
  10. Peng, K., Cai, G., Chen, B. M., Dong, M., Lum, K. Y., and Lee, T. H. “Design and Implementation of an Autonomous Flight Control Law for a UAV Helicopter,” Automatica, Vol. 45, No. 10, pp. 2333-2338, October 2009.##
  11. Awais, Y. and Iqbal, A. “Robust Altitude Tracking of a Helicopter using Sliding Mode Control Structure”; Proc. Int. Conf. Emerging Technologies.  Islamabad, Pakistan, 2012.##
  12. Nodland, D., Zargarzadeh, H., and Jagannathan, S. “Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter UAV,” IEEE Transactions on Neural Networks and Learning Systems, Vol 7, pp. 1061-1073, 2013.##
  13. Camacho, E.F. and Bordons, C. “Model Predictive Control,” New York Springer-Verlag, Berlin, 1998.
  14. Wang, L. “Model Predictive Control System Design and Implementation Using MATLAB,” First Edition, Springer, London, 2009.##
  15. Wang, L. “Discrete Model Predictive Controller Design Using Laguerre Functions”; Journal of Process Control, Vol. 14, No. 2, pp. 131–142, 2004.##
  16. Valencia, G. and Rossiter, J.A. “Using Laguerre Functions to Improve Efficiency of Multi-Parametric Predictive Control”; Proc. American. Conf. Control.  Baltimore, USA, 2010.##
  17. Xiang, X. L., Wang, Y. and Wang. “Laguerre Function Based Multiple Model Predictive Control for Nonlinear System”;  Proc. Int. Conf. Automation Logistics. Beijing, China, 2001.##
  18. Padfield, G.D. “Helicopter Flight Dynamics,” Second Edition, Blackwell, 2007.##
  19. Wahlberg, B. “System Identification Using Laguerre Models," IEEE Transactions on Automatic Control, Vol. 36, pp. 551–562, 1991.##
  20. US Army Aviation and Troop Command. “Aeronautical Design Standard Performance Specification Handling Qualities Requirements for Military Rotorcraft,” ADS-33D-PRF, 1996.##
  21. Doyle, J.C., Francis, B.A., and Tannenbaum, A. “Feedback Control Theory,” Macmillan, New York, 1992.##
Volume 15, Issue 1 - Serial Number 55
September 2020
Pages 25-38
  • Receive Date: 03 September 2018
  • Revise Date: 19 February 2019
  • Accept Date: 19 September 2018
  • Publish Date: 21 April 2019