Unmanned Aerial Vehicles (UAVs) flight control system is the core of the UAV system, the performance of UAV depends largely on the UAV flight control system’s performance. UAV is a high nonlinear, fast time-varying, and strong coupled system, whose accurate mathematical model is difficult to be obtained and operating environment is full of disturbance such as sensor noise and atmospheric turbulence. How to overcome the uncertainty and undesired disturbance to obtain robust and high-performance flight control laws is the most important issue in the flight control system design for UAV. Traditional linear design methods have been difficult to meet the requirements of modern flight control. Nonlinear model-based UAV control law design has already been focused by more and more researchers. In this dissertation, based on nonlinear flight control law design for UAVs, robust nonlinear flight control problem is deeply researched and the following progresses have been made. 1. The effects of nonlinearity, uncertainty in UAVs model and atmospheric turbulence to UAVs are analyzed. 2. A robust attitude control algorithm is proposed based on Extended State Observer and feedback linearization via differential geometry. The attitude system is decoupled into linear plants with the existences of uncertainty and extern disturbance. In order to achieve robust and accurate attitude control, Extended State Observers are used to estimate uncertainty and extern disturbance so as to compensate them in control law design. A robust coordinated turn controller is designed based on the proposed algorithm. The validity and advantages of the algorithm and the coordinated turning controller are demonstrated by numerical simulations. 3. A maneuver control algorithm for UAVs is proposed based on Backstepping algorithm and nonlinear H∞control theory. The control laws and cost functions are constructed by the designing flexibility of Backstepping algorithm so that the closed-loop systems and the cost functions satisfy Hamilton-Jacobi-Isaacs(HJI) equations, thus guaranteeing the closed-loop systems asymptotic tracking ability and disturbance attenuation ability. Simulation results show the validity of the algorithm. 4. For a class of unknown nonlinear system, an adaptive neural network based H∞tracking control algorithm which can handle the constraints on control inputs is proposed. Then a robust flight control algorithm is designed using neural network based H∞tracking co...
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