Robust neuro-H/sub /spl infin// controller design for aircraft auto-landing

A robust neuro-control scheme is presented for aircraft auto-landing under severe wind conditions and partial loss of control surfaces. In the scheme, a dynamic radial basis function network (RBFN) called minimal resource allocating network (MRAN), that incorporates a growing and pruning strategy, is utilize to aid an H/sub /spl infin// controller using a feedback-error-learning mechanism. The neural network uses only online learning and is not trained "a priori". Specifically, the performance of this neuro-controller for aircraft auto-landing in a microburst along with a partial loss of control effectiveness is analyzed and compared with other control schemes. Simulation studies show that the performance obtained by the neuro-H/sub /spl infin// control scheme is better than the other control schemes under failure and extreme wind conditions.

[1]  Mitsuo Kawato,et al.  Neural network control for a closed-loop System using Feedback-error-learning , 1993, Neural Networks.

[2]  Kevin A. Scharfenberg.  POLARIMETRIC RADAR OBSERVATIONS OF A MICROBURST-PRODUCING THUNDERSTORM DURING STEPS , 2000 .

[3]  A. J. Calise.  Neural networks in nonlinear aircraft flight control , 1995 .

[4]  Anthony J. Calise.  Nonlinear Aircraft Flight Control , 1996 .

[5]  Norihiko Adachi,et al.  An intelligent landing system based on a human skill model , 1998 .

[6]  N. Sundararajan,et al.  Fully Tuned Radial Basis Function Neural Networks for Flight Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[7]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[8]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[9]  J. Svoboda,et al.  Design of a reconfigurable automated landing system for VTOL unmanned air vehicles , 2000, Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093).

[10]  Marcello R. Napolitano,et al.  On-line learning non-linear direct neuro controllers for restructurable control systems , 1994 .

[11]  Keith Glover,et al.  The application of scheduled H∞ controllers to a VSTOL aircraft , 1993, IEEE Trans. Autom. Control..

[12]  D. Subbaram Naidu,et al.  Unified approach to H/sub 2/ and H/sub /spl infin// optimal control of a hypersonic vehicle , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[13]  Charles C. Jorgensen,et al.  A neural network baseline problem for control of aircraft flare and touchdown , 1990 .

[14]  David G. Ward,et al.  #00 Anti-Windup Design for Linear Systems Subject to Input Saturation * , 2002 .

[15]  Li Yan,et al.  Neuro-Flight Controllers for Aircraft Using Minimal Resource Allocating Networks (MRAN) , 2001, Neural Computing & Applications.

[16]  Marcello R. Napolitano,et al.  On-Line Learning Nonlinear Direct Neurocontrollers for Restructurable Control Systems , 1995 .

[17]  J-S Yee,et al.  Non-fragile H∞ flight controller design for large bank-angle tracking manoeuvres , 2000 .

[18]  M. Steinberg,et al.  Applying neural network technology to future generation military flight control systems , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.