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barrier-Based Test Synthesis for Safety-Critical Systems Subject to Timed Reach-Avoid Specifications

IEEE TRANSACTIONS ON AUTOMATIC control 2025年第4期70卷 2180-2195页

作者： Akella, Prithvi Ahmadi, Mohamadreza Murray, Richard M. Ames, Aaron D. CALTECH Control & Dynam Syst CDS Pasadena CA 91125 USA TuSimple San Diego CA 92122 USA

We propose an adversarial, time-varying test-synthesis procedure for safety-critical systems without requiring specific knowledge of the underlying controller steering the system. Specifically, our approach codifies the system objective as a timed reach-avoid specification. Then, by coupling control barrier functions with this class of specifications, we construct an instantaneous difficulty metric whose minimizer corresponds to the most difficult test at that system state. By defining tests as the minimizer of this difficulty metric over the space of allowable tests, we provably identify realizable and maximally difficult tests of system behavior. Finally, we develop this test-synthesis procedure for both continuous and discrete-time systems and showcase our test-synthesis procedure on simulated and hardware examples.

关键词： control systems Robustness Time-varying systems Search problems Safety Logic Automobiles Trajectory Quadrupedal robots Postal services control barrier functions formal methods minimax problems safety-critical systems test and evaluation

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Neural-network-based accelerated safe Q-learning for optimal control of discrete-time nonlinear systems with state constraints☆

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NEURAL NETWORKS 2025年 186卷 107249页

作者： Zhao, Mingming Wang, Ding Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

For unknown nonlinear systems with state constraints, it is difficult to achieve the safe optimal control by using Q-learning methods based on traditional quadratic utility functions. To solve this problem, this article proposes an accelerated safe Q-learning (SQL) technique that addresses the concurrent requirements of safety and optimality for discrete-time nonlinear systems within an integrated framework. First, an adjustable control barrier function is designed and integrated into the cost function, aiming to facilitate the transformation of constrained optimal control problems into unconstrained cases. The augmented cost function is closely linked to the next state, enabling quicker deviation of the state from constraint boundaries. Second, leveraging offline data that adheres to safety constraints, we introduce an off-policy value iteration SQL approach for searching a safe optimal policy, thus mitigating the risk of unsafe interactions that may result from suboptimal iterative policies. Third, the vast amounts of offline data and the complex augmented cost function can hinder the learning speed of the algorithm. To address this issue, we integrate historical iteration information into the current iteration step to accelerate policy evaluation, and introduce the Nesterov Momentum technique to expedite policy improvement. Additionally, the theoretical analysis demonstrates the convergence, optimality, and safety of the SQL algorithm. Finally, under the influence of different parameters, simulation outcomes of two nonlinear systems with state constraints reveal the efficacy and advantages of the accelerated SQL approach. The proposed method requires fewer iterations while enabling the system state to converge to the equilibrium point more rapidly.

关键词： Accelerated value iteration Adaptive dynamic programming control barrier functions Neural networks State constraints Safe Q-learning

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Safety-Critical Optimal control of Discrete-Time Non-Linear Systems via Policy Iteration-Based Q-Learning

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第8期35卷 2863-2881页

作者： Long, Lijun Liu, Xiaomei Huang, Xiaomin Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

This paper investigates the problem of safety-critical optimal control for discrete-time non-linear systems. A safety-critical control algorithm is developed based on Q-learning and an iterative adaptive dynamic programming, that is, policy iteration. Discrete-time control barrier functions (CBFs) are introduced into the utility function for guaranteeing safety, in which a novel definition of the safe set and its boundary with multiple discrete-time CBFs are given. Also, for discrete-time systems, by using multiple discrete-time CBFs, the safety-critical optimal control problem of multiple safety objectives is addressed. Meanwhile, safety, convergence, and stability of the developed algorithm are rigorously demonstrated. An effective method to obtain an initial safety-admissible control law is established. Also, the developed algorithm is implemented by building an actor-critic structure with neural networks. Finally, the effectiveness of the proposed algorithm is illustrated by three simulation examples.

关键词： control barrier functions discrete-time systems neural networks Q-learning safety-critical control

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An asymmetric allocation rule of constraints derived from control barrier function for collision avoidance between autonomous robots

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SICE JOURNAL OF control MEASUREMENT AND SYSTEM INTEGRATION 2025年第1期18卷

作者： Dan, Hayato Etchu, Hiroki Yokoi, Kanon Origane, Yuki Kurabayashi, Daisuke Inst Sci Tokyo Sch Engn Tokyo Japan

This paper addresses the challenge of collision avoidance in multi-agent autonomous robotic systems. To design the control input in a distributed manner, traditional methods using control barrier functions (CBFs) for collision avoidance propose allocation rules of the constraints, which are about the control inputs and are derived from CBFs to avoid the collision. However, these methods sometimes suffer from the infeasibility of Quadratic-programming (QP)-based controllers and noise due to the need for relative velocity information. To overcome these limitations, we first derive the necessary conditions for the allocation rule that decouples constraints derived from CBFs to guarantee the collision avoidance. We then propose a novel allocation rule that asymmetrically decouples the constraints, that satisfies the derived conditions. This allocation rule can enhance the feasibility of controllers and eliminate the need for relative velocity information. Finally, we demonstrate its effectiveness through simulations and real-world experiments. The results show that the proposed method successfully ensures collision avoidance, maintaining safety distances without relying on relative velocity data. Our contributions include: (1) defining the conditions for the allocation rule, (2) proposing a constraint allocation rule excluding relative velocity, (3) validating the feasibility of controllers through simulations, and (4) demonstrating real-world applicability with experiments, thus offering a practical solution for autonomous robotic systems.

关键词： Multi-agent systems autonomous robot collision avoidance control barrier functions constraint-based control

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Adaptive sliding mode and safety control for excavators using Kinematic control barrier Function and sliding mode disturbance observer

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AUTOMATION IN CONSTRUCTION 2025年 172卷

作者： Huang, Weidi Wang, Qi Yang, Shuwei Zhang, Junhui Xu, Bing Zhejiang Univ State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China

Excavators have complex structures, large load, and often works in scenarios with safety hazards. Existing methods overlook control-level safety and over-prioritize accuracy, neglecting input smoothness. To address these challenges, a barrier functions Adaptive Sliding Mode (BFASM) safety control method based on Kinematic control barrier Function (KCBF) and Sliding Mode Disturbance Observer (SMDO) is proposed. Specifically, a virtual motion trajectory tracking controller is established and CBF provides safe joint velocity inputs for system. A barrier Function (BF)-based anti-saturation adaptive sliding mode controller is proposed. SMDO is used to estimate the lumped disturbance. BF is used to design the bounded adaptive control gain to ensure that sliding variables remain in predefined neighborhoods of zero, and its size does not depend on disturbance boundary. Simulation results demonstrate that the proposed safety controller effectively ensures safety and the tracking controller keeps errors within a predefined range of 1 degrees with no chattering of control inputs.

关键词： Excavator control barrier functions Adaptive sliding mode control barrier functions Sliding mode disturbance observer

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Model-free predictive iterative learning safety-critical consensus control for multi-agent systems with randomly varying trial lengths

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SYSTEMS & control LETTERS 2025年 196卷

作者： Xu, Rui Chen, Hua Tang, Yao Long, Xinyuan Hohai Univ Sch Math Nanjing 210098 Peoples R China

In this paper, a model-free predictive iterative learning control (MFPILC) is proposed for a class of nonlinear and nonaffine single-input multiple-output (SIMO) discrete-time multi-agent systems with randomly varying trial lengths, input time-varying delay and nonrepetitive external disturbances to deal with the safety-critical consensus control problem. First, the agent dynamics are transformed into a novel dynamic linearization data model along the iteration axis containing the upper and lower bound of the unknown input time delay with an unknown system parameter pseudo gradient (PG). A steepest-descent estimation algorithm updated along the iterative learning axis is applied to deal with the unknown PG and the unknown non-repetitive external disturbances. Further, the predictive compensation mechanisms are proposed to address the problem of data loss caused by the varying trial lengths at each iteration of the MASs. Therefore, a predictive iterative learning consensus control method is designed for MASs based on consensus error and data compensation mechanisms. Moreover, a new model-free predictive iterative learning safety-critical consensus control combined with a discrete-time iterative learning control barrier function (ILCBF) is presented to ensure the output safety of nonlinear MASs. Finally, simulation results further verify the effectiveness of the proposed method.

关键词： Multi-agent systems(MASs) Iterative learning consensus control Predictive control control barrier functions Safety-critical control Time-delay

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Haptic Shared control Framework with Interaction Force Constraint Based on control barrier Function for Teleoperation

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SENSORS 2025年第2期25卷 405-405页

作者： Qin, Wenlei Yi, Haoran Fan, Zhibin Zhao, Jie Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

Current teleoperated robotic systems for retinal surgery cannot effectively control subtle tool-to-tissue interaction forces. This limitation may lead to patient injury caused by the surgeon's mistakes. To improve the safety of retinal surgery, this paper proposes a haptic shared control framework for teleoperation based on a force-constrained supervisory controller. The supervisory controller leverages control barrier functions (CBFs) and the interaction model to modify teleoperated inputs when they are deemed unsafe. This method ensures that the interaction forces at the slave robot's end-effector remain within the safe range without the robot's dynamic model and the safety margin. Additionally, the master robot provides haptic feedback to enhance the surgeon's situational awareness during surgery, reducing the risk of misjudgment. Finally, simulated membrane peeling experiments are conducted in a controlled intraocular surgical environment using a teleoperated robotic system controlled by a non-expert. The experimental results demonstrate that the proposed control framework significantly reduces the rate of force constraint violation.

关键词： shared control force constraint control barrier functions teleoperation medical robots and systems

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Safe Reinforcement Learning for Event-Triggered control of Automated Vehicles With Uncertainty

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Hu, Fengqing Fu, Xiaowen Huang, Hailong Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

Safety is of vital importance to transportation cyber-physical systems (T-CPS). With the deeper integration of automated vehicles (AVs) in road traffic, safe control algorithms for AVs have become a crucial demand for maintaining the overall safety of T-CPS. One of the major concerns with respect to the DRL-based cyber systems of AVs is the lack of safety guarantees, which may cause physical collisions. Safe reinforcement learning (SRL) is a promising way to enhance the safety performance of traditional DRL. However, current SRL methods are designed in a time-triggered (TT) paradigm, where the controller is uniformly triggered in the time domain. Despite the safety enhancement, this TT-SRL paradigm lacks data efficiency in safety-critical scenarios, and most importantly, still cannot guarantee safety during the intervals between time steps. The main objective of this paper is to address the safety issue of the existing TT-SRL methods while improving the data efficiency by proposing an event-triggered SRL (ET-SRL) framework. In the proposed method, safety is guaranteed by designing an additional safe controller to correct the unsafe actions generated by DRL. Event-triggered control barrier functions (CBFs) are used to provide safety constraints in a discrete manner. A state and dynamics synchronization module is also designed to estimate model uncertainties considering both the state dynamics and the input dynamics. The proposed method is trained and applied in simulated car-following experiments. The effectiveness of the proposed method is demonstrated by comparison to both DRL-based and model-based baselines in both artificial safety-critical scenarios and a real trajectory dataset.

关键词： Safety Training Vehicle dynamics Event detection Reinforcement learning Artificial intelligence Aerospace electronics Uncertainty Time-domain analysis Road traffic Event-triggered control safe reinforcement learning control barrier functions automated vehicles

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A resilient active target defense strategy using multiple UAVs with nonlinear dynamics

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年

作者： Li, Bochen Wang, Chenggang Ding, Lu Huang, Dan Han, Xiaodong Song, Lei Zhang, Youan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Guangxi Univ Sch Elect Engn Nanning Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai Peoples R China

In this study, a resilient defense strategy (RDS) is proposed for a team of unmanned aerial vehicles (UAVs) with nonlinear dynamics to protect the target in a multiple defender multiple attacker active target defense differential game. To keep a defender's dominance over an attacker, a control barrier function (CBF)-based controller is designed based on the geometric relationships between players' dominant regions formed by the defender-attacker game and the attacker-target game, which is applicable to players with nonlinear control-affine dynamics. This controller integrates a forward-invariant constraint that connects nonlinear players' control variables to their dominant regions, ensuring the defense winning condition in a 1-defender-1-attacker game. To dynamically allocate the defenders against potential intelligent attackers with evasive maneuvers in a multiple-defender-multiple-attacker game, resilience is integrated with a designed reallocation mechanism to form the RDS. When any of the allocated defenders no longer takes the dominant position, a reallocation of the defenders is triggered. In terms of the defense winning rate, the results show that the RDS outperforms the distance-based task allocation method with a pure pursuit controller. Moreover, a higher defender-to-attacker speed ratio helps improve the winning rate of the defenders, while the disadvantaged maneuverability (angular turn rates) of the defenders does not degrade the winning rate when there are sufficient defenders.

关键词： Unmanned aerial vehicles active target defense control barrier functions resilience set invariance dominant regions

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Adaptive safety-critical control for a class of nonlinear systems with parametric uncertainties: A control barrier function approach

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SYSTEMS & control LETTERS 2024年 188卷

作者： Wang, Yujie Xu, Xiangru Univ Wisconsin Madison Dept Mech Engn Madison WI 53706 USA

This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control -input matrices. The method combines control barrier functions and adaptive laws to generate a safe controller through a nonlinear program with an explicitly given closed -form solution. The proposed approach verifies the non -emptiness of the admissible control set independently of online parameter estimations, which can ensure that the safe controller is singularity -free. A data -driven algorithm is also developed to improve the performance of the proposed controller by tightening the bounds of the unknown parameters. The effectiveness of the control scheme is demonstrated through numerical simulations.

关键词： Safety-critical control control barrier functions Adaptive control Data-driven approach Nonlinear programming

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