Control de posición de un UAV mediante una estrategia de control predictivo para labores de monitoreo

Abstract

This work involves the implementation of model predictive control (MPC) method to control a UAV position. In this project, a parrot A.R Drone 2.0 is used. The predictive control algorithm used in this project is the state space MPC, which depends on the state space model of each controlled variables. This algorithm was developed for different control variables: altitude, roll, pitch and yaw. The respective simulations were made by Matlab software for subsequent implementation in the Labview software. The primary goal of predictive control is to solve the problems of process control efficiently. These processes may exhibit complex dynamic behaviors, such as the coupled variables, instability and even restrictions on some variables. The strategy of this type of control is to use a mathematical model of the process and thus obtain a series of predictions of the future behavior of the process. Based on these predictions and a reference for the controlled variable, future control signals are calculated so that the variable converge in their respective reference values, and respecting different restrictions in the system variable. To meet the objective of controlling the position of the drone, the project is developed in several phases, which are: obtain mathematical models of each variable to monitor and control each of them separately. Initially develop and test predictive control algorithm for SISO state-space system, checking the operation, simulated and implemented in the drone. The second step is the implementation of the algorithm for a MIMO system, which will control the position of the UAV.