Real-time validation and comparison of fuzzy identification and state-space identification for a UAV platform

Unmanned aerial vehicles (UAVs) have been playing an increasingly important role in military and civilian applications. Identification of UAV model is an important process in the controller design. In this paper, identification of the attitude dynamics of UAV is investigated. Two different identification techniques for attitude dynamics of UAV are applied, verified and compared together. The first method is based on an error mapping approach, while the second one is based on fuzzy system approach. The main features of the two identification methods are discussed and compared. The identification algorithms are programmed onto the microcontroller and a real time validation was performed using the in-house developed hardware in loop simulation (HIL) tool. The performance of both identification approaches is evaluated based on the flight data. Real time simulation results show that the fuzzy identification approach is better than error mapping approach

[1]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[2]  Sergey Edward Lyshevski.  State-space identification of nonlinear flight dynamics , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.

[3]  Stefan Werner,et al.  Single-computer HWIL simulation facility for real-time vision systems , 1998, Defense, Security, and Sensing.

[4]  Hossein Salehfar,et al.  A systematic approach to linguistic fuzzy modeling based on input-output data , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[5]  Andrew A. Goldenberg,et al.  Development of a systematic methodology of fuzzy logic modeling , 1998, IEEE Trans. Fuzzy Syst..

[6]  Reza Langari,et al.  Complex systems modeling via fuzzy logic , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[7]  George C. Mouzouris,et al.  Nonsingleton fuzzy logic systems: theory and application , 1997, IEEE Trans. Fuzzy Syst..

[8]  A. Leonessa,et al.  Nonlinear identification of marine thruster dynamics , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[9]  Eugene A. Morelli,et al.  System IDentification Programs for AirCraft (SIDPAC) , 2002 .

[10]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[11]  G N Roberts,et al.  Fuzzy clustering methods for identifying and modelling of non-linear control strategies , 2001 .

[12]  S. Lyashevskiy,et al.  Nonlinear identification of aircraft , 1996, Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro.

[13]  Sergey Edward Lyshevski,et al.  Nonlinear identification of induction motor parameters , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[14]  Li-Xin Wang.  Design and analysis of fuzzy identifiers of nonlinear dynamic systems , 1995, IEEE Trans. Autom. Control..

[15]  Saeed Gazor,et al.  Hybrid adaptive fuzzy identification and control of nonlinear systems , 2002, IEEE Trans. Fuzzy Syst..

[16]  Claire J. Tomlin,et al.  Autopilot Design for the Stanford DragonFly UAV: Validation through Hardware-in-the-Loop Simulation* , 2001 .

[17]  Eric N. Johnson,et al.  FLIGHT SIMULATION FOR THE DEVELOPMENT OF AN EXPERIMENTAL UAV , 2002 .

[18]  A. Tiano.  Comparison of non linear identification methods for underwater vehicles , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[19]  Sergey Edward Lyshevski,et al.  Identification of nonlinear flight dynamics: theory and practice , 2000, IEEE Trans. Aerosp. Electron. Syst..