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Design of a T Factor Based RBFNC for a Flight Control System

DOI: 10.1155/2014/897691

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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. 1. Introduction The conventional design methods of a control system often require mathematical models describing the dynamic behavior of the plant to be controlled. When such mathematical models are difficult to obtain due to uncertainty or complexity, the conventional techniques based on mathematical models are not well suited. Artificial neural network (ANN) in last decade has become popular for plant identification and control [1, 2]. An advantage of the ANN is its ability to handle the nonlinear mapping of the input-output space. It is well known that back propagation based ANN suffers from local minima and over fitting problems which is difficult to be implemented in real time due to a large number of neurons in the hidden layer in comparison to the RBFNC [3, 4]. Since early 1990s, radial basis function network with Gaussian function has been widely used as the basic structure of neural network in nonlinear control [5–7]. Locally tuned and overlapping receptive fields have been found in cerebral cortex visual cortex and in other parts of the brain. The concept of localized information processing in the form of receptive fields suggests that such local learning offers alternative computational opportunities to learning with global basis functions [8]. Gomi and Kawato proposed a feedback error learning control strategy, where a Gaussian RBFN is used for online learning of the inverse dynamics of the system [6]. A Radial basic function neural controller (RBFNC) with learning mechanism is


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