Design of a T Factor Based RBFNC for a Flight Control System
Author(s) -
Chandra Sekhar Mohanty,
Partha Sarathi Khuntia,
Debarati Mitra
Publication year - 2014
Publication title -
advances in artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 1687-7489
pISSN - 1687-7470
DOI - 10.1155/2014/897691
Subject(s) - computer science , settling time , control theory (sociology) , controller (irrigation) , factor (programming language) , function (biology) , trajectory , pitch control , pid controller , control system , control (management) , control engineering , step response , artificial intelligence , engineering , aerospace engineering , temperature control , agronomy , physics , electrical engineering , turbine , evolutionary biology , astronomy , biology , programming language
This paper presents the design of modified radial basic function neural controller (MRBFNC) for the pitch control of an aircraft to obtain the desired pitch angel as required by the pilot while maneuvering an aircraft. In this design, the parameters of radial basis function neural controller (RBFNC) are optimized by implementing a feedback mechanism which is controlled by a tuning factor “α” (T factor). For a given input, the response of the RBFN controller is tuned by using T factor for better performance of the aircraft pitch control system. The proposed system is demonstrated under different condition (absence and presence of sensor noise). The simulation results show that MRBFNC performs better, in terms of settling time and rise time for both conditions, than the conventional RBFNC. It is also seen that, as the value of the T factor increases, the aircraft pitch control system performs better and settles quickly to its reference trajectory. A comparison between MRBFNC and conventional RBFNC is also established to discuss the superiority of the former techniques
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