Quadrocopter control by LQR algorithm

With the increasing use of unmanned aerial vehicles, control of these vehicles has become increasingly important today. Various methods are used to control the three Degree of freedom four rotor quadrocopter system (3 DOF Hover) which is one of these vehicle types. The quadrocopter system has a multi-input, multi-output (MIMO) structure and a difficult to control due to exposure of external factors. Analytical methods such as Linear Quadratic Regulator LQR, Linear Quadratic Gausian (LQG), H-infinity and Amplified Linear Quadratic Regulator (ALQR) are used in the literature to control such helicopter models. In this thesis study, a second order LQR control method was used to control 3-DOF Quadrocopter systems. The Q and R weight matrices in the LQR control method are determined by using Darwinian Particle Swarm Optimization (DPSO) and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) methods. The obtained weight matrices are tested on the simulation model of a 3-DOF four-rotor quadrocopter system. Results are then examined in real time on a 3-DOF quadrocopter prototype, produced by Quanser, that produces good control performance in simulation. Thus, the effects of the correct determination of the Q and R weight matrices on the control performance is determined. KEYWORDS: LQR, Optimization, 3 DOF Hover, Quadrocopter, Unmanned Aerial Vehicles