Model predictive control with application to real-time hardware and guided parafoil

Abstract

Model Predictive Control (MPC) is a control strategy that is suitable for optimizing the performance of constrained systems. Constraints are present in all control systems due to the physical and environmental limits on plant operation. Through a systematical handling of constraints, MPC can improve the performance of a system by allowing it to safely operate near constraint boundaries. This thesis describes the mathematical background of MPC and develops two controllers. One controller is based on a linear model of the plant and is successfully applied to a real-time 3 degrees-of-freedom helicopter system, used to simulate helicopter-like motions in a laboratory setting. This system has a number of significant state and control constraints. The second controller uses a nonlinear model and is applied to a guided parafoil to identify the advantage of using a Doppler wind sensor. A method for reducing the computational load is also introduced that is applicable to both controllers.