Robust distributed model predictive control
Robust distributed model predictive control
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DOI：
10.1080/00207170701491070
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发表时间：
2007-09
影响因子：
2.1
通讯作者：
Arthur G. Richards;J. How
中科院分区：
文献类型：
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作者：
Arthur G. Richards;J. How
提出了一种耦合约束系统的分布式模型预测控制（DMPC）方法。该方法将单个大型规划优化问题分解为多个较小的子问题，每个子问题仅针对特定子系统的控制进行规划。相关的计划数据之间的子问题，以确保所有的决策满足耦合约束。新算法保证所有的优化仍然是可行的，耦合约束将得到满足，每个子系统将收敛到它的目标，尽管未知的，但有界的干扰的行动。仿真结果表明，新的算法提供了显着减少的计算时间，只有一个小的性能下降相比，集中式MPC。
This paper presents a formulation for distributed model predictive control (DMPC) of systems with coupled constraints. The approach divides the single large planning optimization into smaller sub-problems, each planning only for the controls of a particular subsystem. Relevant plan data is communicated between sub-problems to ensure that all decisions satisfy the coupled constraints. The new algorithm guarantees that all optimizations remain feasible, that the coupled constraints will be satisfied, and that each subsystem will converge to its target, despite the action of unknown but bounded disturbances. Simulation results are presented showing that the new algorithm offers significant reductions in computation time for only a small degradation in performance in comparison with centralized MPC.