Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system.

► The paper deals with the model predictive control (MPC) of integrating systems with dead-time (time delay).
► The controller is based on a state space model that is equivalent to the analytical step response of the system.
► It is considered the practical case of zone control of the outputs and optimizing targets for the inputs and/or outputs.
► Stability is achieved by considering an infinite output horizon.
► Two versions of the controller are tested through simulation of a multivariable industrial reactor system.