Abstract
This paper presents a robust non-iterative distributed model predictive control algorithm for spatially interconnected systems. The system consists of multiple linear discrete-time subsystems, which are weakly coupled in their states. Each subsystem is subject to state and input constraints as well as disturbances. The predictive model of each subsystem updates its state, depending on its own state and the neighboring states. The disturbance and the model error are handled via robustness constraints. The recursive feasibility of the optimization problems and the stability of the closed-loop systems are established. Finally, the effectiveness and advantages of the proposed algorithm are verified via simulations.
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