控制理论(社会学)
模型预测控制
约束(计算机辅助设计)
线性矩阵不等式
计算机科学
欺骗
最优化问题
集合(抽象数据类型)
鲁棒控制
理论(学习稳定性)
数学优化
国家(计算机科学)
控制(管理)
数学
控制系统
工程类
法学
算法
人工智能
机器学习
电气工程
几何学
程序设计语言
政治学
作者
Jun Wang,Baocang Ding,Jianchen Hu
标识
DOI:10.1109/tac.2020.2984221
摘要
This article considers security control under the network environment for a system with deception attacks, polytopic uncertainty, and persistent bounded disturbance. Since the state is immeasurable and the physical constraint is considered, the output feedback robust model predictive control (MPC) is utilized. The previous results on the output feedback robust MPC are extended to address the deception attacks. The mean-square quadratic boundedness is defined in order to characterize the closed-loop stability. An optimization problem is proposed, which can be solved by linear matrix inequality techniques. By a simple refreshment of the state estimation error set, the optimization problem is shown to be recursively feasible, and the augmented state converges to the neighborhood of equilibrium point. The effectiveness of the proposed theoretical approach is demonstrated by a numerical simulation example.
科研通智能强力驱动
Strongly Powered by AbleSci AI