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Control Barrier Functions (CBF) have provided a very versatile framework for the synthesis of safe control architectures for a wide class of nonlinear dynamical systems. Typically, CBF-based synthesis approaches apply to systems that…

系统与控制 · 电气工程与系统科学 2024-02-15 Shuo Yang , Mitchell Black , Georgios Fainekos , Bardh Hoxha , Hideki Okamoto , Rahul Mangharam

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree…

Learning-based control with safety guarantees usually requires real-time safety certification and modifications of possibly unsafe learning-based policies. The control barrier function (CBF) method uses a safety filter containing a…

系统与控制 · 电气工程与系统科学 2024-10-25 Kanghui He , Shengling Shi , Ton van den Boom , Bart De Schutter

Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is lacking. Recent work has attempted to merge control…

系统与控制 · 电气工程与系统科学 2026-05-07 Zachary Olkin , William D. Compton , Aaron D. Ames

This paper proposes an optimization with penalty-based feedback design framework for safe stabilization of control affine systems. Our starting point is the availability of a control Lyapunov function (CLF) and a control barrier function…

最优化与控制 · 数学 2022-07-26 Pol Mestres , Jorge Cortés

A DC microgrid is a promising alternative to the traditional AC power grid, since it can efficiently integrate distributed and renewable energy resources. However, as an emerging framework, it lacks the rigorous theoretical guarantees of…

系统与控制 · 电气工程与系统科学 2026-02-13 Muratkhan Abdirash , Xiaofan Cui

Control barrier functions (CBFs) provide a principled framework for enforcing safety in control systems -- yet the certified safe operating region in practice is often conservative, especially under input bounds. In many applications,…

系统与控制 · 电气工程与系统科学 2026-04-07 Pio Ong , David E. J. van Wijk , Massimiliano de Sa , Joel W. Burdick , Aaron D. Ames

Adaptive control has focused on online control of dynamic systems in the presence of parametric uncertainties, with solutions guaranteeing stability and control performance. Safety, a related property to stability, is becoming increasingly…

系统与控制 · 电气工程与系统科学 2023-09-12 Johannes Autenrieb , Anuradha M. Annaswamy

This paper considers the safety-critical control design problem with output measurements. An observer-based safety control framework that integrates the estimation error quantified observer and the control barrier function (CBF) approach is…

最优化与控制 · 数学 2023-01-24 Yujie Wang , Xiangru Xu

This paper proposes a novel Taylor-Lagrange Control (TLC) method for nonlinear control systems to ensure the safety and stability through Taylor's theorem with Lagrange remainder. To achieve this, we expand a safety or stability function…

系统与控制 · 电气工程与系统科学 2025-12-16 Wei Xiao , Anni Li

Ensuring safety for autonomous systems under uncertainty remains challenging, particularly when safety of the true state is required despite the true state not being fully known. Control barrier functions (CBFs) have become widely adopted…

系统与控制 · 电气工程与系统科学 2026-01-26 Ruoyu Lin , Magnus Egerstedt

Quadratic programs (QP) subject to multiple time-dependent control barrier function (CBF) based constraints have been used to design safety-critical controllers. However, ensuring the existence of a solution at all times to the QP subject…

Safety is a fundamental requirement of control systems. Control Barrier Functions (CBFs) are proposed to ensure the safety of the control system by constructing safety filters or synthesizing control inputs. However, the safety guarantee…

机器人学 · 计算机科学 2024-03-29 Manan Tayal , Hongchao Zhang , Pushpak Jagtap , Andrew Clark , Shishir Kolathaya

Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both continuous states and discrete switchings. Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for…

机器人学 · 计算机科学 2024-12-02 Shuo Yang , Yu Chen , Xiang Yin , George J. Pappas , Rahul Mangharam

This tutorial provides a critical review of the practical application of Control Barrier Functions (CBFs) in robotic safety. While the theoretical foundations of CBFs are well-established, I identify a recurring gap between the mathematical…

机器人学 · 计算机科学 2026-03-10 Taekyung Kim

Safety is one of the most important properties of control systems. Sensor faults and attacks and actuator failures may cause errors in the sensor measurements and system dynamics, which leads to erroneous control inputs and hence safety…

系统与控制 · 电气工程与系统科学 2025-06-30 Hongchao Zhang , Zhouchi Li , Andrew Clark

Control barrier functions (CBFs) offer an efficient framework for designing real-time safe controllers. However, CBF-based controllers can be short-sighted, resulting in poor performance, a behaviour which is aggravated in uncertain…

系统与控制 · 电气工程与系统科学 2024-09-16 Allan Andre do Nascimento , Antonis Papachristodoulou , Kostas Margellos

This paper presents extensions of control barrier function (CBF) and control Lyapunov function (CLF) theory to systems wherein all actuators cause impulsive changes to the state trajectory, and can only be used again after a minimum dwell…

最优化与控制 · 数学 2023-06-09 Joseph Breeden , Dimitra Panagou

In safety-critical control systems, ensuring both system safety and smooth control input is essential for practical deployment. Existing Control Barrier Function (CBF) frameworks, especially High-Order CBFs (HOCBFs), effectively enforce…

系统与控制 · 电气工程与系统科学 2026-03-11 Shuo Liu , Wei Xiao , Calin A. Belta

This paper presents a safe controller synthesis of discrete-time stochastic systems using Control Barrier Functions (CBFs). The proposed condition allows the design of a safe controller synthesis that ensures system safety while avoiding…

系统与控制 · 电气工程与系统科学 2025-01-17 Sotaro Fushimi , Kenta Hoshino , Yuki Nishimura