Persistently exciting model predictive control

Model predictive control (MPC) is a well-known and widely used advanced control technique, which is model-based and capable of handling both input and state/output constraints via receding horizon optimization methods. Fundamentally, MPC is a nondynamic or memoryless state feedback control. Because of its use of a model, MPC should be amenable to adaptive implementation and to on-line tuning of the model. Such an approach requires guaranteeing signal properties, known as 'persistent excitation', to ensure uniform identifiability of the model, often expressed in terms of spectral content or 'sufficient richness' of a periodic input. We propose an approach to augment the input constraint set of MPC to provide this guarantee. This, in turn, requires equipping the controller with its own state to capture the control signal history. The feasibility of periodic signals for this condition is established. A computational example is presented illustrating the technique and its properties. Copyright © 2013 John Wiley & Sons, Ltd.