Modeling and Nonlinear Control of a Quadcopter for Stabilization and Trajectory Tracking

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Abstract
This paper presents an adequate mathematical representation of a quadcopter’s system dynamics and effective control techniques. A quadcopter is an unmanned aerial vehicle (UAV) that is able to do vertical take-off and landing. This study presents a nonlinear quadcopter system’s mathematical modeling and control for stabilization and trajectory tracking. The mathematical model of the system dynamics of the quadcopter is derived using Newton and Euler equations with proper references to the appropriate frame or coordinate system. A PD control algorithm is developed for the nonlinear system for stabilization. Another nonlinear control technique called full state feedback linearization (FBL) using nonlinear dynamic inversion (NDI) is developed and implemented on the quadcopter system. However, there is a problem with the normal approach of the complete derivation of the full state FBL system using NDI as gathered from the literature review. In such an approach, the PD controller that was used for attitude stabilization was able to stabilize the angles to zero states, but the position variables cannot be stabilized because the state variables are not observable. Thus, a new approach where the position variables are mapped to the angle variables which are controllable so as to drive all states to zero stability was proposed in this study. The aim of the study was achieved but the downside is that it takes a longer time to achieve this stability so it is not efficient and should only be considered when absolute zero stability is the aim without considering time efficiency. The study further investigates the problem of nonlinear quadcopter system’s mathematical modelling and control for stabilization and trajectory tracking using the feedback linearization (FBL) technique combined with the PD controller. The proposed control algorithms are implemented on the quadcopter model using MATLAB and analyzed in terms of system stabilization and trajectory tracking. The PD controller produces satisfactory results for system stabilization, but the FBL system combined with the PD controller performs better for trajectory tracking of the quadcopter system.
Funding Information
  • Covenant University

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