Adaptive Fuzzy Gain Scheduling in Guidance System Design

A dynamic backpropagationtraining algorithm for an adaptive fuzzy gain scheduling feedback control scheme was developed. This novel design methodologyuses a Takagi–Sugeno fuzzy system to represent the fuzzy relationship between the scheduling variables andcontrollerparameters (Takagi,T., and Sugeno,M., “Fuzzy IdentiŽ cation of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, 1985, pp. 116–132). Direct realistic extension applicable to the guidance system design is introduced. This application relates to terminal guidance design for guided missiles. Mach number, altitude, and time to go are used as measured, time-varying exogenous scheduling variables injected into the guidance law. Results from homing-loopsimulations show that the presented approach offers better terminal guidance performance than the conventional proportional navigationguidance design, that is, less control effort and a smaller miss distance.

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

[2]  Ciann-Dong Yang,et al.  H/sub /spl infin// gain scheduling using fuzzy rules , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[3]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[4]  Ching-Fang Lin,et al.  Modern Navigation, Guidance, And Control Processing , 1991 .

[5]  D. Luenberger.  Optimization by Vector Space Methods , 1968 .

[6]  Douglas A. Lawrence,et al.  Missile autopilot design using a gain scheduling technique , 1994, Proceedings of 26th Southeastern Symposium on System Theory.

[7]  M. Tomizuka,et al.  Fuzzy logic control for lateral vehicle guidance , 1993, IEEE Control Systems.

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Gary J. Balas,et al.  Design of robust, time-varying controllers for missile autopilots , 1992, [Proceedings 1992] The First IEEE Conference on Control Applications.

[10]  R.-H. Liang,et al.  Scheduling of hydroelectric generations using artificial neural networks , 1994 .

[11]  Shaohua Tan,et al.  Gain scheduling: from conventional to neuro-fuzzy , 1997, Autom..

[12]  Jeff S. Shamma,et al.  Trajectory scheduled missile autopilot design , 1992, [Proceedings 1992] The First IEEE Conference on Control Applications.

[13]  Toshiyuki Tanaka,et al.  A robust gain scheduler interpolated into multiple models by membership functions , 1992 .

[14]  Overtaking optimal control and gain scheduling , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[15]  Gwo-Ruey Yu,et al.  Gain scheduling for lateral motion of propulsion controlled aircraft using neural networks , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[16]  Chun-Liang Lin,et al.  A novel approach to guidance and control system design using genetic-based fuzzy logic model , 2002, IEEE Trans. Control. Syst. Technol..

[17]  G. L. Zacharias,et al.  Fuzzy logic gain scheduling for flight control , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[18]  Wilson J. Rugh,et al.  Analytical Framework for Gain Scheduling , 1990, 1990 American Control Conference.

[19]  Andrew G. Sparks,et al.  A gain scheduled multivariable design for a manual flight control system , 1992, [Proceedings 1992] The First IEEE Conference on Control Applications.

[20]  Pramod P. Khargonekar,et al.  Missile Autopilot Designs Using H8 Control with Gain Scheduling and Dynamic Inversion , 1998 .

[21]  K. Sobel,et al.  Application of gain scheduled eigenstructure assignment to flight control design , 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.