Economic Model Predictive Control of Sampled-Data Linear Systems With Piecewise Constant Control

The paper studies an economic model predictive control (EMPC) problem for sampled-data linear systems with system constraints. The cost function consists of an economic part and a regulatory part, and a new EMPC algorithm with piecewise constant control is designed. Iterative feasibility of the designed optimization problem and input-to-state stability (ISS) of the closed-loop system are proved. In particular, we show that the closed-loop system is input-to-state stable with respect to the supremum norm of the economic cost, and the system state is ultimately bounded within a bound determined by the economic cost. Through thorough simulations, the effectiveness of the designed algorithm is verified and the tradeoff between control and economic performance is demonstrated.