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In this paper, we proposed a nonlinear robust control design combined with neural network for a 3 degree of freedom (DOF) helicopter test-bed which may be subjected to unknown external disturbance and contains structure uncertainties. Regulation and tracking control design are proposed for the angles of elevation, pitch and travel axes. Numerical simulation results are provided to illustrate that...
For an aircraft flying in atmosphere a BP neural network controller based on the active control technology is designed. The design goal is to reject the influence of a rotary gust disturbance to the normal overload of the aircraft. In order to improve the dynamic response of such aircraft, the active control technology which will act on the longitudinal control is proposed. The designed controller...
The objective of this paper is to design an optimized controller for the tri-turbofan airship model. In lieu of using the traditional controller analysis method, the particle swarm optimization algorithm for controller optimization has been implemented. For more accurate results, this research used an updated neural network model to approximate the actual tri-turbofan airship dynamics. The effectiveness...
Artificial Neural Networks (ANNs) are widely applied nowadays for classification, identification, control, diagnostics, recognition, etc. They can be implemented for identification of dynamic systems. The concept of ANN is highly used in design and simulation of control system of Unmanned Aerial Vehicles (UAVs). Controller design for UAV is subject to time varying and non-linear model parameters....
This paper proposes the implementation of fuzzy cerebellar model arithmetic computer (FCMAC) neural network for altitude and velocity tracking control of the longitudinal model of an airbreathing hypersonic cruise vehicle (AHCV) which has an integrated airframe-propulsion system configuration. A set of nonlinear longitudinal equations of motion for the vehicle which include the CFD-generated aerodynamic,...
The MAV is very little, so it is easily disturbed by the environment and has week stability. Due to its low Reynolds number, it is easy to be affected by the unstable air (turbulence and gusts), and other outside interference. Recent work in dynamic inversion with neural network may be applied to control a MAV where the reference commands include position, velocity, attitude and angular rate. This...
The paper considers the problem of wing load control problems. To stabilize and control the wing load of the aircraft, an active wing load control system is designed. The dynamic model inversion is used as the feedback linearization method of choice. To dispose the model drift and instability of the aerodynamic model, an on line adaptive neural networks is introduced to reconstruct the inversion error...
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