An Adaptive H Controller Design for Bank-To-Turn Missiles Using Ridge Gaussian Neural Networks

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with stabilization.

[1]  Y. Shrivastava,et al.  Adaptive H∞ neural network tracking controller for electrically driven manipulators , 1998 .

[2]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[3]  George W. Irwin,et al.  Hybrid neural adaptive control for bank-to-turn missiles , 1997, IEEE Trans. Control. Syst. Technol..

[4]  Yuan Xu,et al.  Constructive methods of approximation by ridge functions and radial functions , 1993, Numerical Algorithms.

[5]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[6]  Frank L. Lewis,et al.  A neural network controller for feedback linearization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[7]  A. Astolfi.  Disturbance Attenuation and H,-Control Via Measurement Feedback in , 1992 .

[8]  P. Khargonekar,et al.  Robust stabilization of uncertain linear systems: quadratic stabilizability and H/sup infinity / control theory , 1990 .

[9]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[10]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[11]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[12]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[13]  S. J. Koffman,et al.  Gaussian network variants: a preliminary study , 1993, IEEE International Conference on Neural Networks.

[14]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[15]  Dongkyoung Chwa,et al.  New parametric affine modeling and control for skid-to-turn missiles , 2001, IEEE Trans. Control. Syst. Technol..

[16]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[17]  Li-Chen Fu,et al.  Nonlinear autopilot and guidance for a highly maneuverable missile , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[18]  Wilson J. Rugh,et al.  Gain scheduling for H-infinity controllers: a flight control example , 1993, IEEE Trans. Control. Syst. Technol..

[19]  Anthony J. Calise,et al.  Adaptive nonlinear control of agile antiair missiles using neural networks , 2000, IEEE Trans. Control. Syst. Technol..

[20]  Tang-Kai Yin,et al.  Fuzzy model-reference adaptive control , 1995, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Li-Chen Fu,et al.  Adaptive Robust Autopilot Design for Bank-to-Turn Aircraft , 1993, 1993 American Control Conference.

[22]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[23]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[24]  Jennie Si,et al.  The best approximation to C/sup 2/ functions and its error bounds using regular-center Gaussian networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[25]  George W. Irwin,et al.  Neural model reference adaptive control and application to a BTT-CLOS guidance system , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).