Abstract
In this paper, state variables estimation and Fuzzy Sliding Mode Control (FSMC) are presented in order to estimate the state variables and altitude-attitude tracking control in presence of internal and external disturbances for unmanned quadrotor. The main idea of the proposed control strategy is the development of an Extended Kalman Filter (EKF) for the observation of the states. Fuzzy logic systems are used to adapt the unknown switching-gains to eliminate the chattering phenomenon induced by Sliding Mode Control (SMC). The stability of the system is guaranteed in the sense of Lyapunov. The effectiveness and robustness of the proposed controller-observer scheme that takes into account internal and external disturbances are demonstrated on computer simulation using Matlab environment.
Highlights
In recent years, Unmanned Aerial Vehicles (UAVs) have became a topic of interest in many research organizations due to their wide applications in several areas, such as enforcement of traffic rules and road networks surveillance, industrial plants and high-tension power lines, mapping three-dimensional environments
In [23], a novel adaptive fuzzy gain-scheduling sliding mode control is studied for attitude regulation of an unmanned quadrotors with parametric uncertainties and external disturbances
In [25], the authors designed a fuzzy sliding mode control based on backstepping synthesis for unmanned quadrotors, in which the observation problem was not considered
Summary
In recent years, Unmanned Aerial Vehicles (UAVs) have became a topic of interest in many research organizations due to their wide applications in several areas, such as enforcement of traffic rules and road networks surveillance, industrial plants and high-tension power lines, mapping three-dimensional environments. Several control algorithms have been proposed to altitude and attitude control of unmanned quadrotor systems such as backstepping based controller [5], fuzzy logic based controller, and sliding mode controller [6] and [7]. The elaboration of a control law for this system often requires access to the value of one or more of its states For this reason, it is necessary to design an auxiliary dynamic system named observer, capable to deliver state estimates from the measurements provided by physical sensors and applied inputs. EKF provides the suboptimal state estimator for its ability to consider the stochastic uncertainties, which is the case of quadrotor UAVs. EKF is a recursive algorithm, and it is known for its high convergence rate, which improves transient performance significantly.
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