In this paper,an adaptive neural backstepping control method based on barrier Lyapunov function is proposed for hypersonic vehicle considering full state constraints.The longitudinal dynamic of hypersonic vehicle can ...In this paper,an adaptive neural backstepping control method based on barrier Lyapunov function is proposed for hypersonic vehicle considering full state constraints.The longitudinal dynamic of hypersonic vehicle can be divided into two subsystems,i.e.,altitude subsystem and velocity subsystem and the controllers are designed with backstepping method,respectively.In the designing process,the radial basis function neural networks are used to approximate the unknown nonlinear functions of longitudinal dynamic,therefore,the accuracy requirement of hypersonic vehicle model is largely reduced.In order to handle the explosion of complexity issues occurring in the backstepping method,a tracking differentiator is introduced to calculate the differential of virtual control law.The barrier Lyapunov function is constructed to overcome the full system dynamic state constraints and an auxiliary system is designed for overcome the input state saturation issue.The stability is carried out based on Lyapunov theory,and the signals of closed-loop system established are uniformly ultimately bounded.The simulation results show that the controller designed for hypersonic vehicle can guarantee the good tracking performance.展开更多
A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with...A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.展开更多
This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforce...This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.展开更多
The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response...The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response,this paper proposes an adaptive robust controller based on an asymmetric barrier Lyapunov function(ABLF).The controller design incorporates both load and driver states through a backstepping synthesis.The overshoot and lag of barrel position errors are constrained within asymmetric boundaries,accounting for complex rotational uncertainties via an adaptive law and linear extended state observers(LESO).Simulations and experiments under typical artillery operating conditions validate the effectiveness and dynamic tracking performance of the proposed control strategy in comparison with other methods.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
This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bound...This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.展开更多
In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and ful...In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered system.According to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be applied.Then,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear terms.The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero.Meanwhile,it is shown that the constraint requirement on the system will not be violated during the operation.Finally,two simulation examples are provided to show the effectiveness of the proposed control scheme.展开更多
基金supported by the National Natural Science Foundation of China(No.61903374).
文摘In this paper,an adaptive neural backstepping control method based on barrier Lyapunov function is proposed for hypersonic vehicle considering full state constraints.The longitudinal dynamic of hypersonic vehicle can be divided into two subsystems,i.e.,altitude subsystem and velocity subsystem and the controllers are designed with backstepping method,respectively.In the designing process,the radial basis function neural networks are used to approximate the unknown nonlinear functions of longitudinal dynamic,therefore,the accuracy requirement of hypersonic vehicle model is largely reduced.In order to handle the explosion of complexity issues occurring in the backstepping method,a tracking differentiator is introduced to calculate the differential of virtual control law.The barrier Lyapunov function is constructed to overcome the full system dynamic state constraints and an auxiliary system is designed for overcome the input state saturation issue.The stability is carried out based on Lyapunov theory,and the signals of closed-loop system established are uniformly ultimately bounded.The simulation results show that the controller designed for hypersonic vehicle can guarantee the good tracking performance.
基金supported in part by the National Natural Science Foundation of China(6202530361973147)the LiaoNing Revitalization Talents Program(XLYC1907050)。
文摘A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.
基金Project supported by the National Natural Science Foundation of China(Nos.62203392 and 62373329)the Natural Science Foundation of Zhejiang Province,China(No.LY23F030009)the Baima Lake Laboratory Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBMHD24F030002)。
文摘This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.
文摘The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response,this paper proposes an adaptive robust controller based on an asymmetric barrier Lyapunov function(ABLF).The controller design incorporates both load and driver states through a backstepping synthesis.The overshoot and lag of barrel position errors are constrained within asymmetric boundaries,accounting for complex rotational uncertainties via an adaptive law and linear extended state observers(LESO).Simulations and experiments under typical artillery operating conditions validate the effectiveness and dynamic tracking performance of the proposed control strategy in comparison with other methods.
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。
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.501JCGG2024129003,501JCGG2024129005,501JCGG2024129006),the Fundamental Research Funds for the Central Universities,China(No.YWF-24-JC-09)the National Key Research and Development Program of China(No.2018YFC1506401)。
文摘This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.
基金supported in part by the National Natural Science Foundation of China under Grant No.62303278in part by the Taishan Scholar Project of Shandong Province of China under Grant No.tsqn201909078。
文摘In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered system.According to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be applied.Then,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear terms.The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero.Meanwhile,it is shown that the constraint requirement on the system will not be violated during the operation.Finally,two simulation examples are provided to show the effectiveness of the proposed control scheme.
