A Library on the Robot Operating System (ROS) for Model Predictive Controlimplementation

Sammanfattning: Model Predictive Control is a receding horizon control technique that is based on making predictionsin the future for a determined number of steps, using a model of the system to be controlled. Thisthesis report is centered around Model Predictive Control (MPC) and its application. In this thesis,there are two main goals: firstly, is the development of a software structure that uses the properties ofObject Oriented Programming (OOP) and the Robot Operative System (ROS) to ease the use of MPCapplications. Secondly, the use and verification of the capabilities of MPC controllers in plants with fastdynamics, such as the quadrotor. A linearized model of the quadrotor is developed for the controllerto perform the predictions, and the non-linear version is used to make a numerical simulator to test theapplication. The MPC software structure works as it successfully integrates information from the classesthat represent the model and optimization method to solve the quadratic problem. The resulting MPCcontroller shows a good response when following simple trajectories in the presence of simulated noise.However, when more complex trajectories are used, a considerable offset from the reference is obtained.Such behavior mostly caused by the use of a very limited model, which demonstrates the considerablesensibility of the controller to the accuracy of the used model.