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
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
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 reinforc...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
该文研究孤岛交流微电网二次电压和频率的固定时间精确控制问题,基于多智能体一致性方法,提出考虑状态受限的自适应模糊固定时间二次电压控制器和基于控制障碍函数的二次频率控制器。在多智能体一致性控制中,将每一个分布式电源视为一...该文研究孤岛交流微电网二次电压和频率的固定时间精确控制问题,基于多智能体一致性方法,提出考虑状态受限的自适应模糊固定时间二次电压控制器和基于控制障碍函数的二次频率控制器。在多智能体一致性控制中,将每一个分布式电源视为一个非线性智能体,智能体之间通过稀疏网络进行通信。在电压控制器设计中,采用反馈线性化后未知变量的自适应模糊估计提高控制器的自适应能力,并引入新的滑模面使电压控制器在固定时间内收敛。考虑到系统状态受限问题,分别采用障碍Lyapunov函数和控制障碍函数设计电压与频率控制器,使系统状态在预设的约束范围内。频率控制器的设计还考虑了有功功率的精确分配问题,给出了严格的固定时间收敛及稳定性证明。在Matlab/Sim Power System环境下,对微电网负载变化及大干扰下的仿真验证了所提控制器的有效性。展开更多
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.
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.
文摘该文研究孤岛交流微电网二次电压和频率的固定时间精确控制问题,基于多智能体一致性方法,提出考虑状态受限的自适应模糊固定时间二次电压控制器和基于控制障碍函数的二次频率控制器。在多智能体一致性控制中,将每一个分布式电源视为一个非线性智能体,智能体之间通过稀疏网络进行通信。在电压控制器设计中,采用反馈线性化后未知变量的自适应模糊估计提高控制器的自适应能力,并引入新的滑模面使电压控制器在固定时间内收敛。考虑到系统状态受限问题,分别采用障碍Lyapunov函数和控制障碍函数设计电压与频率控制器,使系统状态在预设的约束范围内。频率控制器的设计还考虑了有功功率的精确分配问题,给出了严格的固定时间收敛及稳定性证明。在Matlab/Sim Power System环境下,对微电网负载变化及大干扰下的仿真验证了所提控制器的有效性。
