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Robust control barrier functions based on active disturbance rejection control for adaptive cruise control 认领 引用
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作者 Jaime Arcos-Legarda Andres Hoyos Hernán García Arias 《Control Theory and Technology》 EI CSCD 2025年第3期454-463,共10页
The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, un... The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, unknown dynamics, and external disturbances. The proposed method combines control barrier functions and control Lyapunov functions with a nonlinear extended state observer to produce a robust and safe control strategy for dynamic systems subject to uncertainties and disturbances. This control strategy employs an optimization-based control, supported by the disturbance estimation from a nonlinear extended state observer. Using a quadratic programming algorithm, the controller computes an optimal, stable, and safe control action at each sampling instant. The effectiveness of the proposed approach is demonstrated through numerical simulations of a safety-critical interconnected adaptive cruise control system. 展开更多
关键词 Control barrier functions Active disturbance rejection control Extended state observer Control Lyapunov function Optimization-based control Quadratic programming
A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems 认领 引用 被引量：12
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作者 Boqian Li Shiping Wen +2 位作者 Zheng Yan Guanghui Wen Tingwen Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第3期584-602,共19页
This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem ... This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area. 展开更多
关键词 Control barrier function(CBF) control Lyapunov function(CLF) nonlinear-affine control systems
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Constrained Moving Path Following Control for UAV With Robust Control Barrier Function 认领 引用 被引量：7
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作者 Zewei Zheng Jiazhe Li +1 位作者 Zhiyuan Guan Zongyu Zuo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第7期1557-1570,共14页
This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind disturbances.The vehicle is required to converge to a reference path moving with re... This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind disturbances.The vehicle is required to converge to a reference path moving with respect to the inertial frame,while the path following error is not expected to violate the predefined boundaries.Differently from existing moving path following guidance laws,the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem.A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance observers.To guarantee that the path following error will not exceed the prescribed bounds,a robust control barrier function is developed and incorporated into controller design with quadratic program based framework.The proposed method does not require the initial position of the UAV to be within predefined boundaries.And the safety margin concept makes error-constraint be respected even if in a noisy environment.The proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-theloop(HIL)experiments. 展开更多
关键词 Moving path following(MPF) robust control barrier function safety margin shipboard landing unmanned aerial vehicle(UAV)
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Multi-agent system motion planning under temporal logic specifications and control barrier function 认领 引用 被引量：2
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作者 Xinyuan HUANG Li LI Jie CHEN 《Control Theory and Technology》 EI CSCD 2020年第3期269-278,共10页
In this paper,w e provide a novel scheme to solve the motion planning problem of multi-agent systems under high-level task specifications.First,linear temporal logic is applied to express the global task specification... In this paper,w e provide a novel scheme to solve the motion planning problem of multi-agent systems under high-level task specifications.First,linear temporal logic is applied to express the global task specification.Then an efficient and decentralized algorithm is proposed to decom pose it into local tasks.M oreover,w e use control barrier function to synthesize the local controller for each agent under the linear temporal logic motion plan with safety constraint.Finally,simulation results show the effectiveness and efficiency of our proposed scheme. 展开更多
关键词 Temporal logic multi-agent system formal methods control barrier function
High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems:An Augmented Dynamics Approach 认领 引用
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作者 Haijing Wang Jinzhu Peng +1 位作者 Fangfang Zhang Yaonan Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第12期2487-2496,共10页
Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics.Furthermore,i... Although constraint satisfaction approaches have achieved fruitful results,system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics.Furthermore,it remains a challenge when there are additional constraints on control torques of robotic systems.In this article,we propose a novel high-order control barrier function(HoCBF)-based safety control method for robotic systems subject to input-output constraints,which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque.In our design,augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system,so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics.This proposed scheme leads to the quadratic program(QP),which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law.The proposed closed-loop control system not only achieves the requirements of real-time capability,stability,safety and compliance,but also reduces undesired chattering of control inputs.Finally,the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators. 展开更多
关键词 Augmented dynamics high-order control barrier function(HoCBF) input-output constraints quadratic program(QP) robotic systems
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Dynamic regressor extension and mixing control Lyapunov functions and high-order control Barrier functions 认领 引用
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作者 Peng Sun Jiuxiang Dong 《Journal of Control and Decision》 EI 2026年第2期455-468,共14页
This paper studies the safety-critical tracking control problem for unknown structured systems with bounded disturbances and high relative degree.Firstly,a dynamic regressor extension and mixing high-order control bar... This paper studies the safety-critical tracking control problem for unknown structured systems with bounded disturbances and high relative degree.Firstly,a dynamic regressor extension and mixing high-order control barrier function(DREM-HOCBF)is constructed with a DREM-based robust finite-time parameter identification law.Our approach directly enforces forward invariance of the system containing parameter estimates and worst-case estimation errors to ensure system safety against parameterised uncertainty.Due to DREM,this method can approach the safety set boundary and reduce the conservatism.Meanwhile,it's also applicable to higher relative degree constraints that are more common in actual systems.Then,a DREM control Lyapunov function(DREM-CLF)that leverages the same worst-case estimation errors is proposed to guarantee that the tracking errors converge exponentially.Moreover,the method's robustness against bounded disturbances is considered.Finally,the feasibility of the developed method is verified via an adaptive cruise control(ACC)problem with unknown parameters and bounded disturbances. 展开更多
关键词 Adaptive control safety-critical control dynamic regressor extension and mixing finite-time identification control barrier function adaptive cruise control
Safety-Certified Distributed Formation Control of Networked Autonomous Surface Vehicles by Unifying Control Lyapunov and Control Barrier Functions 认领 引用 被引量：2
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作者 CONG Siming GU Nan +2 位作者 WANG Haoliang WANG Dan PENG Zhouhua 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2025年第2期837-856,共20页
This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs)subject to model uncertainties and environmental disturbances.A safety-certified path-guided coordina... This paper investigates the path-guided distributed formation control of networked autonomous surface vehicles(ASVs)subject to model uncertainties and environmental disturbances.A safety-certified path-guided coordinated control method is proposed for multiple ASVs to achieve a distributed formation in obstacle environments.Specifically,a neural predictor with a high-order tuner is presented to approximate unknown nonlinearities with accelerated learning performance.Subsequently,control Lyapunov functions(CLFs)and control barrier functions(CBFs)are constructed for mapping stability constraints and safety constraints on states to control inputs.A quadratic optimization problem is constructed with the norm of control inputs as the objective function,CLFs and CBFs as constraints.Neurodynamic optimization is used to deal with the quadratic programming problem and generate the optimal kinetic control signals,thereby attaining the desired safe formation.Unlike the high-order CBF,a CBF backstepping method is proposed to establish safety constraints such that repeated time derivatives of system nonlinearities can be avoided.The multi-ASVs system is ensured to be input-to-state safe irrespective of high-order relative degree.Through the Lyapunov theory,the multi-ASVs system is proven to be input-to-state stable.Finally,simulation results are presented to validate the efficacy of the presented safety-certified distributed formation control for networked ASVs. 展开更多
关键词 Autonomous surface vehicles control barrier functions neurodynamic optimization pathguided formation control safety-certified control
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Intelligent Safe Optimal Control Towards Koopman Operator-Driven Nonlinear Systems With Asymmetric State and Input Constraints 认领 引用
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作者 Yalu Su Ding Wang +3 位作者 Mingming Zhao Dan Xiong Yiyong Huang Wei Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1135-1150,共16页
For unknown nonlinear systems subject to asymmetric state and input constraints simultaneously,this article establishes a safe value iteration paradigm to learn an optimal control policy in a data-based manner.Initial... For unknown nonlinear systems subject to asymmetric state and input constraints simultaneously,this article establishes a safe value iteration paradigm to learn an optimal control policy in a data-based manner.Initially,the Koopman operator,instead of the black-box neural network,is applied to extract the inherent dynamics of the controlled systems from the measured data,thereby allowing for explicit analysis of the prediction error.To tackle the issue posed by state and input constraints,a crafted control barrier function is seamlessly incorporated into the canonical utility function,which retains the property of positive definiteness for the asymmetric case.Moreover,the value iteration algorithm with regard to the augmented utility function is adopted to attain a safe optimal controller,where the actor and critic networks are leveraged to approximate the control input and associated value function,respectively.The monotonicity,safety,and stability of the raised algorithm are further verified rigorously.Via performing three experiments on the linear system,the nonlinear system,and the manipulator plant,comparative results are obtained to substantiate the superiority and efficacy of the developed approach in achieving optimal performance and safe guarantee. 展开更多
关键词 Adaptive optimal control constrained value iteration control barrier function discrete-time nonlinear systems reinforcement learning Koopman operator
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Composite anti-disturbance safety control for on-orbit inspection of spacecraft 认领 引用
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作者 Kun WANG Enmei WANG +1 位作者 Bo TIAN Jianzhong QIAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期516-529,共14页
This paper addresses the anti-disturbance safety control problem in spacecraft inspection missions,considering multiple positional obstacle constraints and attitude restrictions,both forbidden and mandatory,with logic... This paper addresses the anti-disturbance safety control problem in spacecraft inspection missions,considering multiple positional obstacle constraints and attitude restrictions,both forbidden and mandatory,with logical relationships.To address this challenge,a novel Composite AntiDisturbance Safety Control(CADSC) method is proposed,which combines control barrier functions with disturbance observers.The proposed CADSC framework achieves guaranteed safety control under complex constraints while explicitly addressing external disturbances and model uncertainties.First,positional obstacles are modeled using quadratic surface equations.At the same time,attitude constraints are formulated with logical operators,incorporating the interactions among star trackers,optical cameras,solar panels,and space environment vectors.Then,safe velocity and angular velocity are computed by solving Quadratic Programming(QP) problems based on the spacecraft's kinematic equations.The simplicity and disturbance-free nature of the kinematic model allow for efficient and accurate solutions to the QP problem,ensuring real-time applicability in mission-critical scenarios.Furthermore,proportional-like position and attitude controllers are developed to track the computed safe velocities.These controllers incorporate disturbance estimation techniques to compensate for external disturbances and model uncertainties,thereby enhancing the spacecraft's robustness.Finally,numerical simulations are conducted to validate the effectiveness of the proposed control strategy. 展开更多
关键词 Composite anti-disturbance control Safety control Control barrier function Disturbance rejection On-orbit inspection
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基于Barrier Lyapunov函数的四旋翼无人机状态受限编队控制 认领 引用
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作者 王锐 杨翔宇 +1 位作者 刘永涛 高晨康 《飞行力学》 CSCD 北大核心 2026年第3期48-54,共7页
针对四旋翼无人机编队控制中存在的非线性动态耦合、编队相对位置误差超调等问题,提出了一种基于障碍李雅普诺夫函数(BLF)的分布式编队控制方法。首先,通过图论描述多机间信息交互拓扑,将四旋翼无人机模型分解为位置与姿态子系统;其次,... 针对四旋翼无人机编队控制中存在的非线性动态耦合、编队相对位置误差超调等问题,提出了一种基于障碍李雅普诺夫函数(BLF)的分布式编队控制方法。首先,通过图论描述多机间信息交互拓扑,将四旋翼无人机模型分解为位置与姿态子系统;其次,结合BLF设计控制器,构建编队相对位置误差项并约束其边界,以提升编队几何构型精度与动态响应平稳性;然后,基于Lyapunov稳定性理论证明闭环系统的渐近稳定性,从而确保编队整体的稳定性;最后,开展了仿真验证。结果表明,所设计的编队控制器能够使一组四旋翼无人机形成指定队形并协同飞行,编队相对位置误差始终保持在预设边界内,验证了所提出方法的有效性。 展开更多
关键词 四旋翼无人机 编队控制 障碍李雅普诺夫函数 约束控制 协同控制
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A Self-Healing Predictive Control Method for Discrete-Time Nonlinear Systems 认领 引用
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作者 Shulei Zhang Runda Jia 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第4期668-682,共15页
In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal cas... In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal case is constructed,which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set.In this controller,the tubes are built based on the incremental Lyapunov function to tighten nominal constraints.To deal with the infeasible controller when abnormal states occur,a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set.This is achieved by an auxiliary softconstrained recovery mechanism that can solve the constraint violation caused by the abnormal states.By extending the discrete-time robust control barrier function theory,it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set.The theoretical properties of robust recursive feasibility and bounded stability are further analyzed.The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process. 展开更多
关键词 Control barrier function nonlinear system process safety robust model predictive control self-healing control
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Barrier-Certified Learning-Enabled Safe Control Design for Systems Operating in Uncertain Environments 认领 引用 被引量：1
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作者 Zahra Marvi Bahare Kiumarsi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第3期437-449,共13页
This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constrai... This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constraints are imposed by not only the ego system’s own physical limitations but also other systems operating nearby.Since the model of the external agent is required to impose control barrier functions(CBFs)as safety constraints,a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents.More specifically,the loss function is defined based on barrier function error,instead of the system model error,and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set.The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF(L-ZCBF),which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations.It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents,which is crucial in safety-critical applications in highly interactive environments.The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area. 展开更多
关键词 Control barrier functions(CBFs) experience replay learning safety-critical systems uncertainty
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Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization 认领 引用
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作者 Guanghao Lyu Zhouhua Peng Jun Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第10期2056-2066,共11页
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie... This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles. 展开更多
关键词 Autonomous surface vehicles(ASVs) high-order control barrier functions neurodynamic optimization parallel model predictive control safety-certified control
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Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints 认领 引用 被引量：3
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作者 Mingming Zhao Ding Wang +1 位作者 Shijie Song Junfei Qiao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第12期2408-2422,共15页
This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an opti... This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal performance.First,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and optimality.Second,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical model.Third,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically proven.To obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative policies.Moreover,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL algorithms.Finally,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method. 展开更多
关键词 Adaptive critic control adaptive dynamic programming(ADP) control barrier functions(CBF) stabilizing value iteration Q-learning(SVIQL) state constraints
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信息-物理融合视角下的多机器人系统安全控制与学习综述 认领 引用
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作者 缪志强 张峻铭 +4 位作者 林琼 占巍巍 王祥科 贺威 王耀南 《信息与控制》 CSCD 北大核心 2026年第2期161-187,共27页
多机器人系统在智能制造、灾难救援与无人作战等场景中展现出显著的协同优势,但随着应用环境从结构化空间走向开放、对抗与高动态场景,系统安全性成为制约其大规模部署的核心瓶颈。基于威胁来源与作用机理,可将多机器人系统的安全问题... 多机器人系统在智能制造、灾难救援与无人作战等场景中展现出显著的协同优势,但随着应用环境从结构化空间走向开放、对抗与高动态场景,系统安全性成为制约其大规模部署的核心瓶颈。基于威胁来源与作用机理,可将多机器人系统的安全问题概括为3个层面:物理空间的避碰安全、系统功能的容错安全及信息空间的网络弹性安全。围绕上述维度,本文从信息-物理融合的统一视角系统梳理了多机器人安全控制与学习的发展脉络,涵盖从启发式方法到形式化控制、从鲁棒容错到分布式弹性控制、从模型驱动到学习增强的技术演进。本文重点综述了安全避碰控制(以控制障碍函数为代表的形式化方法及其数据驱动增强)、容错控制(故障检测诊断、分布式协同容错、系统重构)、弹性控制(攻击检测辨识、安全状态估计、弹性恢复策略)及安全强化学习(安全屏蔽、约束优化、信任机制)4大关键技术方向的研究现状与最新进展。通过对各方向的理论基础、代表性进展及其适用边界进行系统分析,本文揭示当前研究在安全-性能权衡、多源不确定性处理、跨层协同设计与可扩展性方面的核心瓶颈。最后,本文讨论了学习与形式化保证的深度融合、弹性架构及具备自解释性的安全决策等未来发展方向,为多机器人系统的可靠自治提供理论参考与技术指引。 展开更多
关键词 多机器人系统 安全控制 安全学习 信息-物理融合 弹性控制 控制障碍函数
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面向低空安全的约束驱动无人机具身任务执行 认领 引用
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作者 李博宸 王成罡 +4 位作者 黄丹 宋磊 丁璐 袁小虎 刘华平 《指挥与控制学报》 CSCD 北大核心 2026年第2期236-245,共10页
为了实现无人机在低空任务中对实时感知障碍物的安全规避,提出一种基于可微分控制障碍函数的约束驱动端到端安全控制框架。以无人机实时获取的图像等感知信息为输入,利用神经网络学习安全约束与任务约束的松弛程度参数,将其嵌入可微分... 为了实现无人机在低空任务中对实时感知障碍物的安全规避,提出一种基于可微分控制障碍函数的约束驱动端到端安全控制框架。以无人机实时获取的图像等感知信息为输入,利用神经网络学习安全约束与任务约束的松弛程度参数,将其嵌入可微分二次规划生成控制量,推导给出了反向传播的损失函数梯度下降公式。在AirSim平台中构建了仿真场景,并给出了从训练数据集构建到模型测试部署的流程。结果表明,提出方法可在障碍物位置未知的情况下,基于深度图像等感知数据端到端生成控制量,相比于基于全连接网络对控制量进行预测的方法实现了更优的任务执行成功率。 展开更多
关键词 低空安全 无人机 具身智能 任务执行 控制障碍函数
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基于分布式时空解耦MPC的无人机集群协同控制 认领 引用
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作者 常绪成 王敬宇 +3 位作者 朱锋 任高峰 李康 刘代军 《控制与决策》 EI CSCD 北大核心 2026年第6期1540-1552,共13页
针对无人机集群协同控制中的高维非凸优化难题,提出一种分布式时空解耦模型预测控制(DSTMPC)框架.首先,将复杂的时空轨迹优化问题解耦为空间几何规划和时间调度两个序贯而协同的子问题:空间层采用基于迭代线性化和自适应信赖域(ILAC)的... 针对无人机集群协同控制中的高维非凸优化难题,提出一种分布式时空解耦模型预测控制(DSTMPC)框架.首先,将复杂的时空轨迹优化问题解耦为空间几何规划和时间调度两个序贯而协同的子问题:空间层采用基于迭代线性化和自适应信赖域(ILAC)的序贯凸化方法处理避障、避碰等非凸约束,并融合控制障碍函数(CBF)确保实时安全;时间层则将轨迹执行转化为高效凸优化问题,通过分布式一致性协议实现集群同步.然后,基于开环解耦系统,设计一种基于冲突状态观测器的时空协同反馈机制,优化时间层至空间层的闭环优化回路.仿真结果表明,所提出框架在不同复杂度场景下均能够实现良好的控制性能:编队误差稳态收敛至0.37 m以内,障碍物最小间距保持在0.2 m以上,验证了所提出方法的有效性和可扩展性,为大规模集群的高效协同控制提供了一种可行方案. 展开更多
关键词 无人机集群 分布式模型预测控制 自适应信赖域 时空解耦 级联稳定性 控制障碍函数
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面向非线性系统的知识驱动高效安全评判学习控制 认领 引用
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作者 王鼎 李鑫 +1 位作者 赵明明 乔俊飞 《自动化学报》 EI CAS CSCD 北大核心 2026年第8期1757-1768,共12页
针对具有状态约束的离散时间非线性系统,开发一种知识驱动高效安全评判学习控制算法.首先,基于知识迁移技术,利用历史任务中的先验知识来改善评判学习控制算法在当前目标任务中的学习效率.同时,构造一类单调递减的衰减函数以避免先验知... 针对具有状态约束的离散时间非线性系统,开发一种知识驱动高效安全评判学习控制算法.首先,基于知识迁移技术,利用历史任务中的先验知识来改善评判学习控制算法在当前目标任务中的学习效率.同时,构造一类单调递减的衰减函数以避免先验知识影响控制策略的最优性.其次,通过设计合适的障碍函数,成功将具有状态约束的最优调节问题转化为无约束最优调节问题,进而建立一种安全评判学习控制框架.利用所提控制算法,可高效求解一类具有状态约束的非线性系统最优控制问题.此外,为提供严谨的理论依据,给出所提控制算法的收敛性证明以及控制策略的稳定性判别准则.最后,利用两个数值仿真实例验证了所提算法的有效性. 展开更多
关键词 自适应动态规划 控制障碍函数 安全评判学习控制 知识迁移 非线性系统
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视野遮挡下机械臂动态视觉伺服安全预测控制设计 认领 引用
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作者 冒建亮 蒋心怡 +1 位作者 周昕 张传林 《实验室研究与探索》 CAS 北大核心 2026年第5期103-108,共6页
针对动态目标跟踪过程中因目标被遮挡而导致的视觉伺服系统失稳问题,提出一种结合鲁棒控制障碍函数的安全预测控制方法。首先,设计基于滑模干扰观测器的标称预测控制器,以提升系统的跟踪精度。在此基础上,进一步构建具有时变边界的鲁棒... 针对动态目标跟踪过程中因目标被遮挡而导致的视觉伺服系统失稳问题,提出一种结合鲁棒控制障碍函数的安全预测控制方法。首先,设计基于滑模干扰观测器的标称预测控制器,以提升系统的跟踪精度。在此基础上,进一步构建具有时变边界的鲁棒控制障碍函数,有效规避遮挡带来的安全风险。最后,通过构建并求解一个二次规划问题,实现动态跟踪任务中控制性能与安全约束的统一。基于MATLAB与机器人操作系统搭建了一体化仿真与实验验证平台。实验结果表明,该方法在动态跟踪任务中表现出更优的跟踪精度与抗干扰能力,并能持续保障系统安全运行。研究成果为动态遮挡环境下的视觉伺服安全跟踪问题提供了系统性解决方案,其预测控制与鲁棒安全相融合的框架具备良好的工程适用性。 展开更多
关键词 视觉伺服 安全预测控制 鲁棒控制障碍函数
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具有奇异输入输出链路的不确定互联非线性系统分散漏斗控制 认领 引用
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作者 刘玉发 刘海 +2 位作者 刘勇华 赵曜 苏春翌 《控制理论与应用》 EI CAS CSCD 北大核心 2026年第5期1052-1060,共9页
研究了一类具有奇异输入输出链路的不确定互联非线性系统的分散跟踪控制问题.与高阶非线性系统相比,本文考虑的非线性系统具有奇异输入输出链路,无需包含幂次函数结构,使得增加幂次积分法无法直接用于该系统的反馈控制设计.为解决该控... 研究了一类具有奇异输入输出链路的不确定互联非线性系统的分散跟踪控制问题.与高阶非线性系统相比,本文考虑的非线性系统具有奇异输入输出链路,无需包含幂次函数结构,使得增加幂次积分法无法直接用于该系统的反馈控制设计.为解决该控制问题,本文结合反推技术和极限定义,提出了一种基于双边障碍函数的分散漏斗控制策略.具体而言,通过引入双边障碍函数,在无需知晓系统函数先验知识的条件下,确保了闭环系统的预设跟踪性能;同时,援用极限定义,解决了系统中奇异输入输出链路带来的技术挑战.仿真实例验证了该控制算法的有效性. 展开更多
关键词 互联非线性系统 奇异输入输出链路 输出跟踪 漏斗控制 双边障碍函数
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