This paper proposes an adaptive path-following controller of small fixed-wing Unmanned Aerial Vehicles (UAVs) in the presence of wind disturbances, which explicitly considers that wind speed is time-varying. The main idea was to formulate UAVs path-following as control design for systems with parametric uncertainties and external disturbances. Assuming that there is no prior information on wind, the proposed solution is based on the L 1 adaptive control, using linearized model dynamics.This approach makes clear statements for performance specifications of the controller and relaxes the common constant wind velocity assumption. This makes the design more realistic and the analysis more rigorous, because in practice wind is usually time varying (windshear, turbulence and gusting).The path-following controller was demonstrated in flight under wind speed up to 10m/s, representing 50% of the nominal UAV airspeed.

An approach for output feedback [Formula: see text] adaptive control of small Unmanned Aerial Vehicles (UAVs) is presented in this paper. The design is based on a state observer instead of the state predictor. The main advantage is that a full state measurement can be avoided, and the design and implementation of the controller are simplified. Furthermore, since the state space description is maintained, the system dynamics including uncertainties can be specified with physical insight, which simplifies practical applications. The adaptation law borrows insights from the sliding mode control to estimate the unknown bounds of external disturbances. Flight test results for the control of a small UAV show the robustness of the [Formula: see text] adaptive controller to large uncertainties and disturbances.

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This paper proposes an ℒ1 fuzzy adaptive controller for a class of uncertain continuous-time single-input single-output nonaffine nonlinear systems. The structure of this controller is derived based on ℒ1 adaptive control design methodology and integrates a fuzzy system. The latter is used to approximate as best as possible a function of an unknown ideal implicit controller, which provides good results and improves the performance significantly. The ℒ1 fuzzy adaptive controller consists of a predictor, a control law and its adaptive laws. The major advantage of the proposed control scheme is its ability to guarantee uniformly bounded transient and tracking performance for the controlled system. These performance bounds can be rendered arbitrarily small by the systematic choice of design parameters. The effectiveness and feasibility of the proposed ℒ1 fuzzy adaptive controller are examined experimentally in the position control of a pneumatic actuator system.