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P. 140
REFERENCES 129
[90] Tanaka T, Chuang CH. Scheduling of linear controllers [95] Rotondo D. Advances in gain-scheduling and fault tol-
for X-29 by neural network and genetic algorithm. erant control techniques. Berlin: Springer; 2018.
In: AIAA Guidance, Navigation and Control Conf., [96] Palm R, Driankov D, Hellendoorn H. Model based
Baltimore, Md., Aug.7–10, 1995: Collect. Techn. Pap. fuzzy control: Fuzzy gain schedulers and sliding mode
Pt 2. AIAA–95–3270—CP. Washington (D.C.); 1995. fuzzy controllers. Berlin: Springer; 1997.
p. 891–900. [97] Bianchi FD De Battista H, Mantz RJ. Wind turbine con-
[91] Jacobs RA, Jordan MI. Learning piecewise control trol systems: Principles, modelling and gain scheduling
strategies in a modular neural network architecture. design. Berlin: Springer; 2007.
IEEE Trans Syst Man Cybern 1993;23(2):337–45.
[92] Hush DR, Horne BG. Progress in supervised neural net- [98] Bryson Ay, Ho YC. Applied optimal control. Toronto,
works. IEEE Control Syst 1993;10(1):8–39. London: Blaisdell Publishing Company; 1969.
[93] Germeyer YB. Introduction to the theory of operations [99] Morozov NI, Tiumentsev YV, Yakovenko AV. An ad-
research. Moscow: Nauka Publishers; 1971 (in Russian). justment of dynamic properties of a controllable ob-
ject using artificial neural networks. Aerosp MAI J
[94] Brusov VS, Baranov SK. Optimal design of aircraft: A
multipurpose approach. Moscow: Mashinostroyeniye 2002;(1):73–94 (in Russian).
Publishers; 1989 (in Russian).