Saturday , June 23 2018

New Systems for Identification, Estimation and Adaptive Control of the Aircrafts Movement

Avionics Department, Faculty of Electrical Engineering, University of Craiova
107, Decebal Blvd., Craiova, Romania

Romulus LUNGU
Avionics Department, Faculty of Electrical Engineering, University of Craiova
107, Decebal Blvd., Craiova, Romania

Constantin ROTARU
Department of Aviation Integrated Systems, Military Technical Academy
81-83, George Cosbuc Blvd., Bucharest, Romania

Abstract: This paper presents two new systems for identification and neuro-adaptive command with direct applicability to the control of the longitudinal and lateral aircrafts movement and to the rockets’ vertical and horizontal movement. Also, a structure for the parametric estimation and discrete optimal command of the aircrafts’ movement is presented. The design of these structures is based on the algorithms that belong to the authors of this paper. Theoretical results are validated by numerical simulations; the authors obtained Matlab/Simulink models and calculus programs for the identification, neuro-adaptive command, on-line estimation and discrete command of the aircrafts movement, respectively.

Keywords: Adaptive, aircraft, neural network, identification, estimation.

>>Full text
Mihai LUNGU, Romulus LUNGU, Constantin ROTARU, New Systems for Identification, Estimation and Adaptive Control of the Aircrafts Movement, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (3), pp. 273-284, 2011.

1. Introduction

The flight to high attack angles (specific for the fight regimes) is affected by the effects of aerodynamic instability. The complexity and uncertainty in modeling of such non-linear phenomena are the main arguments for designing of evolved adaptive control structures, in these conditions the linear models being too far to describe correctly the dynamics of aircrafts. There is a high degree of uncertainty regarding the flight parameters and flying objects dynamics at high attack angles. Another argument is that the actuators are non-linear and present essential non-linear elements such as the saturation of displacement or the saturation of the mobile elements speed. The stability and handling qualities must also be maintained in conditions of sensors and actuators failures.

Control systems (denoted in this paper with) must have the capability to identify defects, to isolate the damaged elements and to reconfigure the architecture in real time. The failures belong to sensors, actuators or force equipments. Thus, a real time redesign of the control systems is needed. The observers must be easily adaptable; that means that their design algorithms must allow aircrafts state estimation with or without the signals provided by the damaged sensors. In all these situations real-time adaptive control based on neural networks is adequate [1], [2], [3], [4], [5]. The training process of the neural networks is done by using signals provided by the state observers; these state observers have, as input, the tracking error vector.

The area of adaptive control has grown to be one of the richest in terms of algorithms, design techniques, analytical tools, and modifications. Despite the rich literature, the field of adaptive control may easily appear to an outsider as collection of unrelated tricks and modifications. The adaptive flight control systems, which are presented in this paper, were designed to provide invariant aircraft response characteristics throughout aircraft’s atmospheric flight envelope.

The contributions of this paper are: an adaptive control system with neural network and adaptive controller, a complex system for the neuro-adaptive control of the aircraft movements and a system for the parametric estimation and discrete optimal command of aircraft longitudinal and lateral movements. So, the purpose of this paper is the design and software implementation in Matlab/Simulink environment of the three systems. By comparison with other adaptive algorithms from the specific literature, the authors’ adaptive flight control systems are characterized by simplicity, proper function and dynamic stability. The study of the algorithms convergence and stability has been made by the paper authors by complex numerical simulations. The stability of the presented systems is obvious from the analysis of the system responses. The paper is organized as follows: the design of the two new systems for aircrafts identification and neuro-adaptive command are given in section 2; the design of the system for the parametric estimation and discrete optimal command of aircraft longitudinal and lateral movements is presented in section 3; three numerical examples are included in section 4; finally, some conclusions are given in section 5.


  1. CALISE, A. J., Flight Evaluation of an Adaptive Velocity Command System for Unmanned Helicopters, AIAA Guidance, Navigation and Control Conference and Exhibit, vol. 2, 11-14 August, Austin, Texas, 2003.
  2. CALISE, A. J., N. HOVAKYMYAN, M. IDAN, Adaptive Output Control of Nonlinear Systems Using Neural Networks, Automatica, vol. 37, (8), August, 2001, pp. 1201-1211.
  3. CALISE, A. J., B. J. YANG, J. I. CRAIG, Augmentation of an Existing Linear Controller with an Adaptive Element, American Control Conference, Nr. ACC02, IEEE 1331, 2002. In Proceedings, p. 6.
  4. HOSEINI, S. M., M. FARROKHI, A. J. KASHKONEI, Robust Adaptive Control Systems Using Neural Networks, The International Journal of Control, 2006, p. 7.
  5. CALISE, A. J., E. N. JOHNSON, M. D. JOHNSON, J. E. CORBAN, Applications of Adaptive Neural – Networks Control to Unmanned Aerial Vehicles, Journal of Harbin Institute of Technology, 2006, vol. 38, No. 11, pp. 1865-1869.
  6. LYSHEVSKI, S. E., Identification Nonlinear Flight Dynamics: Theory and Practice, IEEE Transactions on Aerospace and Electronic System, vol. 36, no. 2, April, 2000, pp. 383-392.
  7. CHEN, X., W. GAOFENG, Z. WEI, C. SHENG, S. SHILEI, Efficient Sigmoid Function for Neural Networks based FPGA Design, Springer Publisher, 2006.
  8. SHAO, L., J. WANG, S. SHAO, Study on the Fitting Ways of Artificial Neural Networks, Journal of Coal Science and Engineering, vol. 14, no. 2, June, 2008.
  9. LUNGU, M., Sisteme de conducere a zborului, Editura Sitech, Craiova, 2008, p. 329.
  10. LUNGU, R., M. LUNGU, L. DINCA, E. STOENESCU, On-line Parametric Identification and Discrete Optimal Command for the Aircrafts Longitudinal Movement, Proceedings of the 7th WSEAS International Conference on System Science and Simulation in Engineering (ICOSSSE ’08), Venice, Italy, November 21-23, 2008.
  11. LUNGU, R., M. LUNGU, Optimal Control of the Rocket’s Lateral Deviation in Rapport with Equal Signal Line. 6th International Conference on Electro-mechanical and Electro-energetic Systems, September 2007, Chisinau, Moldova Republic.
  12. LEE, T., Y. KIM, Nonlinear Adaptive Flight Control Using Backsteping and Neural Networks Controller,. Journal of Guidance, Control and Dynamics, vol. 24, no. 4, July – Aug, 2007, pp. 675-782.
  13. BALESTRASSI, P. P., E. POPOVA, A. P. PAIVA, J. W. MARANGON LIMA, Design of Experiments on Neural Network’s Training for Nonlinear Time Series Forecasting, Elsevier Journal – Neuro-computing, 72, 2009, pp. 1160-1178.
  14. LUNGU, M., R. LUNGU, N. JULA, C. CEPISCA, M. CALBUREANU, Aspects Regarding a New Method for the Optimal Law’s Synthesis of Aircrafts’ Move, WSEAS Transactions on Circuits and Systems, vol. 7, June 2008, pp. 492-501.
  15. BODNER, V.A., Teoria avtomaticeskogo upravlenia poletom, Izd. Nauka, Moscova, 1964.
  16. DONALD, M. Automatic Flight Control Systems, New York, London, Toronto, Sydney, Tokyo, Singapore, 1990.