Abstract
The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.
Highlights
In recent decades, there has been an increase in the applications of autonomous flying robots or popularly known as drones
Motivated by the disturbance observer-based control (DOBC) feedback linearization in [15], this paper proposes an improved disturbance rejection using intelligent disturbance observer based control
The aim of this paper is to control the quadrotor in the presence of time-varying disturbances, e.g. wind
Summary
There has been an increase in the applications of autonomous flying robots or popularly known as drones. Nonlinear control based on feedback linearization technique produces a linear model representation of the nonlinear quadrotor model over a large set of operating points [5]. These feedback-based techniques may not react directly and fast enough in the presence of strong disturbances These approaches cause the closed-loop transient response to be shaped by the adaptive or robust control components instead of the nominal linear model [12]. To overcome these drawbacks, researchers have proposed a control technique so-called active anti-disturbance control (AADC). A simulation study in [15] proposed a linear disturbance observer to improve the robustness of feedback linearization on quadrotor control. The conclusion of this paper is given in the last section
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