![]() The stability of the closed loop system with a proposed hybrid structure is investigated with the help of the Lyapunov function and the concept of passivity. In order to ensure the stability of identification, the identifier will be trained first with the PSO and the online back-propagation learning algorithm. ![]() In this paper, the ANFIS Network has been used for the first time for continuous and online identification of the system dynamics and calculating SDC. In other words, it provides an adaptive model for different conditions. This method also eliminates the problem of uncertainty and accurate measurement of parameters. The advantage of this method in determination of the SDC is more evident when it is not possible to fully understand the dynamics of the system or external factors and disturbance affect the system. One of the methods for determining the relative dynamics of a nonlinear time-varying system and calculating SDC is the online identification method. The use of State Dependent Riccati equation (SDRE) control involves determining the appropriate SDC form, which requires a complete understanding of the dynamics of the system. The outer loop, provides optimal tracking with a straight line scroll criterion for the GGV. The inner loop has a regulating function that guarantees the stability of motion and rotation equations and reduces the effect of external disturbances. In this regard, a hybrid structure of nonlinear optimal control has been proposed to minimize control effort. In this paper, a new method for controlling and guidance of Guided Gliding Vehicle (GGV) is provided. Simulation and experiment results have been presented and verified. Furthermore, two modules of a controller were designed for achieving the desired performance. This algorithm determines the flight characteristics such as the airspeed, angle of attack and roll angle. In addition, fuzzy logic control was used to classify the shape of wing based on the deflection values. The stereo camera was used to study the characteristics of the flexible wings in wind tunnel testing at wind speeds ranging from 11 to 31 km/h, angles of attack ranging from −20 to 20 degree and roll angles ranging from −10 to 10 degree. The camera was placed at the rear end of the wing structure, and the entire wing was in the view. The deflections of wing were measured for chosen locations of landmarks on a flexible wing using a stereo camera. Two different controllers were used: Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Linear Quadratic Regulator (LQR). The deflection of the wing was extracted from experimental testing, and Fuzzy Logic was used to classify these shapes of the wing. The aim of this paper is to develop a new technique to measure and classify the wing shape to control the airspeed, angle of attack (AOA), and the roll angle using a stereo camera. This paper presents a novel approach to control of a flexible wing UAV using vision system. ![]()
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