Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to environmental variations. This task of accurate modelling has been achieved with a multi-network architecture. The multi-network with dynamic selection technique allows a combination of online and offline neural network models to be used in the architecture where the most suitable outputs are selected based on a given criterion. The neural network models are based on the autoregressive technique. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. The multi-network dynamic selection technique has been validated on real-time hardware in the loop (HIL) simulation and the results show the superiority in performance compared to the individual models.

[1]  Dale Richards,et al.  Supervisory Control of Autonomous UAVs in Networked Environments , 2005 .

[2]  Tapabrata Ray,et al.  Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV , 2007, Australian Conference on Artificial Intelligence.

[3]  I. Schiller,et al.  Mission adaptable autonomous vehicles , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[4]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

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

[6]  L. Ljung,et al.  A system identification perspective on neural nets , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[7]  Toru Fujinaka,et al.  A switching scheme for adaptive control using multiple models , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[8]  Jihong Zhu,et al.  System identification of a small UAV's speeding up process before take-off , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[9]  Eric Gagnon,et al.  Identification of a UAV and Design of a Hardware-in-the-Loop System for Nonlinear Control Purposes , 2005 .

[10]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[11]  J. K. Hedrick,et al.  Design of Nonlinear Model Predictive Controller for a Small Fixed-Wing Unmanned Aerial Vehicle , 2006 .

[12]  John David Anderson,et al.  Introduction to Flight , 1985 .

[13]  M. Benosman,et al.  Adaptive control using multiple models switching and tuning , 2005, 2005 International Conference on Industrial Electronics and Control Applications.

[14]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[15]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[16]  Lennart Ljung,et al.  System identification (2nd ed.): theory for the user , 1999 .

[17]  Sreenatha G. Anavatti,et al.  Real-time multi-network based identification with dynamic selection implemented for a low cost UAV , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[18]  Kimon P. Valavanis,et al.  A framework for fuzzy logic based UAV navigation and control , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  F. Borrelli,et al.  Collision-free UAV formation flight using decentralized optimization and invariant sets , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[20]  Sreenatha G. Anavatti,et al.  Real-Time Neural Network Based Online Identification Technique for a UAV Platform , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[21]  S. A. Salman,et al.  Real-time validation and comparison of fuzzy identification and state-space identification for a UAV platform , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[22]  Sreenatha G. Anavatti,et al.  Comparison of Real-time Online and Offline Neural Network Models for a UAV , 2007, 2007 International Joint Conference on Neural Networks.

[23]  Frank L. Lewis,et al.  Multimodel neural networks identification and failure detection of nonlinear systems , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).