Design of a model predictive controller to control UAVs

In this report a continuous time model predictive controller is applied to the control of an Unmanned Aerial Vehicle (UAV). The purpose of this work, is to see if it is possible to design a good tracking controller that is capable of simulating a highly nonlinear aircraft system. For the simulations a MATLAB Simulink model of the UAV 'Ariel ' is used. The nonlinear Simulink model covers all the aircraft dynamics. The model consists of five inputs, twelve states and twelve outputs. This model is linearized at several different operating conditions, for use with the model predictive controller. The model predictive controller is capable of simulating in real time, making it possible to use this in on-line applications. The Ariel model is linearized to a linear time invariant state space system. The state space model is then extended. This means the outputs of the original model are included in the state vector of the extended model. With this extended state space model, it can be shown that the selected outputs can be tracked by the controller. The controller uses an algorith