In this paper, the cooperative distributed model predictive control (dmpc) problem of a class of complex systems consisting of several subsystems is studied. The states of these subsystems are coupled with each other,...
详细信息
In this paper, the cooperative distributed model predictive control (dmpc) problem of a class of complex systems consisting of several subsystems is studied. The states of these subsystems are coupled with each other, and thus bring challenges for the model predictive control algorithm. Moreover, though the subsystems can communicate with each other, they only can access to the output information of their neighboring subsystems. In this case, Luenberger observers are used to estimate the unknown states and a distributed prediction strategy is established for the studied system. Then, the optimal control of the closed-loop system is realized by designing distributed model predictive controller on the basis of the estimated states. The terminal constraints are introduced in the proposed dmpc algorithm to ensure the iterative feasibility and also the stability of the closed-loop. Finally, the effectiveness of the proposed method is verified by a numerical simulation.
In this paper, experimental results are presented for a distributed model predictive control ( dmpc) scheme applied to a laboratory-scale water distribution system consisting of connected water tanks. The setup is an ...
详细信息
ISBN:
(纸本)9781479974061
In this paper, experimental results are presented for a distributed model predictive control ( dmpc) scheme applied to a laboratory-scale water distribution system consisting of connected water tanks. The setup is an example of coupled dynamical systems whose modular structure makes them candidates for agent based distributed control methods. A set of low cost Raspberry Pi microcomputers connected via a common Ethernet-network is used for the implementation of the dmpc. The dmpc algorithm employs an augmented Lagrangian approach to solve the distributed optimal control problem (OCP) subject to nonlinear system dynamics in continuous-time form and input constraints. In this way, the performance and modular character of the dmpc as well as the individual runtime footprints of the communication and computation with respect to the overall runtime of the distributed algorithm are studied.
暂无评论