In this chapter, a distributed MPC strategy suitable for changing setpoints is described. Based on a cooperative distributed control structure, an extended-cost MPC formulation is proposed, which integrates the problem of computing feasible steady state targets—usually known as Steady State Target Optimizer (SSTO) optimization problem—and the dynamic control problem into a single optimization problem. The proposed controller is able to drive the system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in case of coupled constraints. This is possible thanks to the design of the cost function, which integrates the SSTO, and to the warm start algorithm used to initialize the optimization algorithm. A numerical simulation illustrates the benefits of the proposal.

(2014). Cooperative Distributed MPC integrating a Steady State Target Optimizer . Retrieved from http://hdl.handle.net/10446/169466

Cooperative Distributed MPC integrating a Steady State Target Optimizer

Ferramosca, Antonio;
2014-01-01

Abstract

In this chapter, a distributed MPC strategy suitable for changing setpoints is described. Based on a cooperative distributed control structure, an extended-cost MPC formulation is proposed, which integrates the problem of computing feasible steady state targets—usually known as Steady State Target Optimizer (SSTO) optimization problem—and the dynamic control problem into a single optimization problem. The proposed controller is able to drive the system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in case of coupled constraints. This is possible thanks to the design of the cost function, which integrates the SSTO, and to the warm start algorithm used to initialize the optimization algorithm. A numerical simulation illustrates the benefits of the proposal.
Ferramosca, Antonio; Limon, Daniel; González, Alejandro H.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/169466
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