Abstract
This paper considers a class of large-scale systems which is composed of many interacting subsystems, and each of them is controlled by an individual controller. For this type of system, to improve the optimization performance of the entire closed-loop system in a distributed framework without the entire system’s information or too-complicated network information, connectivity is always an important topic. To achieve this purpose, a distributed model predictive control (DMPC) design method is proposed in this paper, where each local model predictive control (MPC) considers the optimization performance of its strong coupling subsystems and communicates with them. A method to determine the strength of the coupling relationship based on the closed-loop system’s performance and subsystem network connectivity is proposed for the selection of each subsystem’s neighbors. Finally, through integrating the steady-state calculation, the designed DMPC is able to guarantee the recursive feasibility and asymptotic stability of the closed-loop system in the cases of both tracking set point and stabilizing system to zeroes. Simulation results show the efficiency of the proposed DMPC.
Highlights
There is a class of complex large-scale industrial control systems which are composed of many interacting and spatially distributed subsystems, and each subsystem is controlled by an individual controller, where the controllers exchange information with each other through a communication network
Many algorithms and design methods have appeared in the literature for different types of systems and for different problems in the design of Distributed model predictive control (DMPC)
With Definition 2, if the deviation brought by omitting weak-coupling neighbors is controlled in a robust positively invariant (RPI) set φi with control law Ki and simplified model in (7) has control law and state ūi, xi confined in Ui Ki φi, Xi φi, respectively, the local subsystem will have a feasible solution for the optimization
Summary
There is a class of complex large-scale industrial control systems which are composed of many interacting and spatially distributed subsystems, and each subsystem is controlled by an individual controller (e.g., large-scale chemical process [1], smart micro-grid [2,3] systems, distributed generation systems [4]), where the controllers exchange information with each other through a communication network. References [14,36] provide a tube-based DMPC where all interactions are considered as disturbances and each subsystem-based MPC is solved independently It does not exchange the state and input trajectory, but the interaction constraints, to avoid the interaction consistency problem. Reference [41] proposes another method based on global calculations of targeting tracking It does not require a feasible starting point of each distributed predictive controller. These methods provide good references and possible methods for designing a tracking DMPC that considers optimization performance improvement and network connectivity.
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