Distributed model predictive control based on the alternating direction method of multipliers for branching open canal irrigation systems

Zhu, Zheli; Guan, Guanghua; Wang, Kang

doi:10.1016/j.agwat.2023.108372

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…8 Zhu applied the DMPC algorithm based on the alternating direction method of multipliers (ADMM) to the branching open canal irrigation systems. 9 This kind of research has received many applications in practice, such as multi-vehicle formation control and cooperative tracking control. [10][11][12] Compared with centralized control, distributed model predictive control is more complex and difficult to control, requiring more computational, communication and control strategies, as well as more refined design and control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Li,

Su,

Guo

2024

Intl J Robust & Nonlinear

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In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

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