控制理论(社会学)
最小相位
有界函数
线性化
约束(计算机辅助设计)
相(物质)
计算机科学
模块化设计
非线性系统
滤波器(信号处理)
控制(管理)
国家(计算机科学)
航程(航空)
控制系统
理论(学习稳定性)
数学
内部模型
光学(聚焦)
控制工程
集合(抽象数据类型)
状态变量
系统动力学
线性系统
模型预测控制
传递函数
钥匙(锁)
控制器(灌溉)
非线性控制
过程(计算)
参数统计
工程类
作者
Jason J. Choi,Claire J. Tomlin,Shankar Sastry,Koushil Sreenath
标识
DOI:10.1109/tac.2026.3719594
摘要
In emerging control applications involving multiple and complex tasks, safety filters are gaining prominence as a modular approach to enforcing safety constraints. Among various methods, control barrier functions (CBFs) are widely used for designing safety filters due to their simplicity, imposing a single linear constraint on the control input at each state. In this work, we focus on the internal dynamics of systems governed by CBF-constrained control laws. Our key observation is that, although CBFs guarantee safety by enforcing state constraints, they can inadvertently be "unsafe" by causing the internal state to diverge. We investigate the conditions under which the full system state, including the internal state, can remain bounded under a CBF-based safety filter. Drawing inspiration from the input-output linearization literature, where boundedness is ensured by minimum phase conditions, we propose a new set of CBF minimum phase conditions tailored to the structure imposed by the CBF constraint. A critical distinction from the original minimum phase conditions is that the internal dynamics in our setting is driven by a nonnegative virtual control input, which reflects the enforcement of the safety constraint. We include a range of numerical examples, including single-input, multi-input, linear, and nonlinear systems, validating both our analysis and the necessity of the proposed CBF minimum phase conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI