In this paper, a robust controller for attitude stabilization of a small quadrotor helicopter is developed. The TS (Takagi-Sugeno) fuzzy model approach and the [Formula: see text] robust control are combined to produce the proposed algorithm. Besides, disturbances and parametric uncertainties are considered. First, the nonlinear model of the vehicle is linearized around several operating points to obtain the representation of a TS fuzzy model, which represents the nonlinearity of the system dynamics. Then, a robust fuzzy controller is synthesized which guarantees desired control performances. The given controller is designed using numerical tools such as linear matrix inequalities (LMI). Finally, simulation results and real-time experiments are presented to validate the performance of the proposed scheme to robustly stabilize the quadrotor dynamics at the desired reference.
In order to assure maximum energy conversion, the angular velocity of the wind turbine rotor tracks a nominal profile depending on the wind speed. However, conventionally, wind flows present non‐differentiable components due to turbulence and gust winds, which affect the wind energy management. Thus, a fast and robust controller is required to induce such nominal regime for maximum energy transfer. A fractional‐order nonlinear disturbance‐observer (FNDOB) is proposed in this paper to cancel the non‐differentiable components of the wind speed, as well as dynamic uncertainties and other aerodynamic disturbances. The proposed FNDOB is based on continuous fractional sliding modes, assuring that disturbances and uncertainties are exactly compensated in finite‐time. A representative simulation study for a variable‐speed wind turbine is presented to show the reliability of the proposed scheme, and a comparative analysis with respect to a conventional linear disturbance observer based control is presented.
This chapter presents an adaptive control technique to stabilize the attitude dynamics of unmanned aerial vehicle (UAV) type quadrotor in the presence of disturbances and/or uncertainties in the parameters due to changes in the payload, nonlinear actuators, and change in environmental conditions. To address the above problem, MRAC (model reference adaptive control) strategy is used. In this schema, a cost function is defined as a function of the error between the output of the system and a desired response from a reference model. Based on this error, the controller parameters are updated. To guarantee the global asymptotic stability of the system, Lyapunov's theory is applied. Simulation results using MATLAB-Simulink platform are presented to demonstrate the convergence of the controller parameters.
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