Distributed model predictive control with receding-horizon stability constraints

Abstract

This paper presents a distributed model predictive control strategy for interconnected process systems employing predictive asymptotic constraints. The plant-wide control is facilitated by the constructive method of online stabilisations that is applicable to the model predictive controllers (MPC) as receding-horizon stability constraints. The plant-wide process is modeled as a large-scale system formed by the subsystems of different unit operations interconnected to each other. The stability condition for the interconnected system is derived from the asymptotically positive realness constraint (APRC), which is subsequently developed into a receding-horizon stability constraint for MPC. The receding-horizon stability constraint is derived from the APRC by predicting the state and control vectors toward to the end of the predictive horizon. The receding horizon stability constraint is less conservative than the previously developed constraint that applied APRC to the current time step vectors. Simulations are provided for the counter-current washing circuit to demonstrate the efficacy of the presented receding-horizon stability constraint.