In this paper we investigate model predictive control (MPC) based on ARX models. ARX models can be identified from data using convex optimization technologies and is linear in the system parameters. Compared to other model parameterizations this feature is an advantage in embedded applications for robust and automatic system identification. Standard MPC is not able to reject a sustained, unmeasured, non zero mean disturbance and will therefore not provide offset free tracking. Offset free tracking can be guaranteed for this type of disturbances if Δ variables are used or if the state space is extended with a disturbance model state. The relation between the base case and the two extended methods are illustrated which provides good understanding and a platform for discussing tuning for good closed loop performance.
|Title of host publication||Proceedings of the American Control Conference|
|Publication status||Published - 2010|
|Event||American Control Conference (ACC 2010) - Baltimore, MD, United States|
Duration: 3 Jun 2010 → 2 Jul 2010
|Conference||American Control Conference (ACC 2010)|
|Period||03/06/2010 → 02/07/2010|