.png){kind=icon}
返回
Distributed matrix-weighted fusion model predictive control algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2023.12.009.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper is concerned with model predictive control (MPC) for multisensor linear systems. In order to fully utilize information and achieve higher control accuracy, two fusion MPC algorithms are proposed. The first algorithm is named estimation-fusion model predictive controller (E-F MPC), which local estimations are fused and then fusion control input is obtained by the fusion estimation. The second algorithm is named control-fusion model predictive controller (C-F MPC), which fuses the control inputs obtained from local systems. The relationships, differences and complexity of the two algorithms are analyzed. The advantages of E-F MPC are that it is simple and easy to implement with high control accuracy, while the disadvantage is the bad robustness because of the heavy estimation, prediction and control tasks for the fusion node. For C-F MPC, with a small loss of accuracy, network throughput and computational cost of its fusion node will be greatly reduced, which makes it good robustness and flexibility. Simulation analysis verifies the effectiveness and correctness of the conclusion.
Keyword:
Model predictive control (MPC)
Multisensor information fusion
state estimation
期刊
J
IF:
3.7
论文数:
6.5K
被引数:
1.5W
机构
引用论文
分享收藏Near-Optimal Rapid MPC Using Neural Networks: A Primal-Dual Policy Learning Framework使用神经网络的近似最优快速MPC: 原始对偶策略学习框架
OAAI
OAAI分享收藏Roles of the Sodium-Translocating NADH:Quinone Oxidoreductase (Na+-NQR) on Vibrio cholerae Metabolism, Motility and Osmotic Stress Resistance.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data.
IF0
PREAI
分享收藏Observation-Driven Multiple UAV Coordinated Standoff Target Tracking Based on Model Predictive Control基于模型预测控制的观测驱动多无人机协同对峙目标跟踪OAAI
OAAI分享收藏分享收藏
查看更多内容