Model predictive control (MPC) is a valuable tool for incorporating system constraints and performance objectives in controller design. Though traditionally employed in process control, MPC is garnering ever more attention from other industries. However, the standard requirement that the MPC optimization parameters be calibrated to the specific plant hinders implementation in mass-produced systems, such as cars, and in systems whose behavior evolves over time. This problem is especially acute when considering systems with discrete changes in behavior, called switched systems, where very little literature has been established. Examples include spacecraft docking, where there is a switch between contact and non-contact dynamics, and networked systems whose communication lines change over time. This research explores how knowledge of the frequency of behaviour changes, or switches, can be incorporated to relax design requirements. Not only could this work broaden the applicability of MPC, but it promises to lighten computational loads, permitting the use of MPC in systems with limited computational resources. A great deal of work has yet to be done in this direction, with the promise of far-reaching consequences.
Model Predictive Control