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
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
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
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
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
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
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
A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed env...A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.展开更多
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
针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control fo...针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。展开更多
基金supported by the Fondo para el Primer Proyecto of the Comitépara el Desarrollo de la Investigación(CODI)at the Universidad de Antioquia(Grant Number PRV2024-78509)。
文摘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.
基金supported in part by the National Natural Science Foundation of China(62273311,61773351)Henan Provincial Science Foundation for Distinguished Young Scholars(242300421051)。
文摘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.
基金supported in part by the National Natural Science Foundation of China(U22B2046,62073079,62088101)in part by the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(8091B022114)in part by NPRP(NPRP 9-466-1-103)from Qatar National Research Fund。
文摘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.
基金supported in part by the National Natural Science Foundations of China(62173016,62073019)the Fundamental Research Funds for the Central Universities(YWF-23-JC-04,YWF-23-JC-02)。
文摘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.
基金This work was partially supported by the National Natural Science Foundation of China(No.51475334)the National Key Research and Development Program of Science and Technology of China(No.2018YFB1305304)the Shanghai Science and Technology Pilot Project(No.19511132100).
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grant No.52471372in part by the Key Basic Research of Dalian under Grant No.2023JJ11CG008+3 种基金in part by the Doctoral Scientific Research Foundation of Liaoning Province under Grant Nos.2024-BS-012 and 2023-BS-077in part by the Postdoctoral Research Foundation of China under Grant No.2024M751980in part by the Bolian Research Funds of Dalian Maritime University and the Fundamental Research Funds for the Central Universities under Grant Nos.3132024601,3132023508in part by the Open Project of State Key Laboratory of Maritime Technology and Safety under Grant No.SKLMTA-DMU2024Y3。
文摘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.
文摘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.
基金National Natural Science Foundation of China(Nos.62173303 and 62273307)Natural Science Foundation of Zhejiang Province(No.LQ24F030023)。
文摘A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.
基金supported in part by the National Science and Technology Major Project(2021ZD0112302)the National Natural Science Foundation of China(62222301,61890930-5,62021003)。
文摘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.
文摘针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。
