1) Lyapunov-Krosovskii functional
Lyapunov-Krasovskii型泛函
1.
By using of the LMI and Lyapunov-Krosovskii functional,a memoryless adaptive state feedback controller is proposed,which can guarantee the closed-loop system is globally stable in the sense of uniform ultimate boundedness.
结合线性矩阵不等式和Lyapunov-Krasovskii型泛函设计出了一种无记忆自适应状态反馈控制器,并证明此控制器使得闭环系统最终一致有界;仿真例子说明了结论的有效性。
2.
By using the Lyapunov stability theory and Lyapunov-Krosovskii functional,we propose a robust adaptive state feedback controller,which can guarantee that the closed-loop system is globally stable in the sense of uniform ultimate boundedness,and the st.
基于Lyapunov稳定性理论和Lyapunov-Krasovskii型泛函,设计了一种鲁棒自适应状态反馈控制器,并证明了此控制器使得闭环系统一致最终有界,且系统的状态将一致渐近趋于0。
2) Lyapunov-Krasovskii functional
Lyapunov-Krasovskii泛函
1.
By adopting Lyapunov-Krasovskii functional and dissi-pative theory,sufficient conditions are given to ensure the existence of a memoryless state feedback control law,which guarantees the stability of the closed-loop system.
采用Lyapunov-Krasovskii泛函和耗散性理论,给出了保证闭环系统渐近稳定的无记忆状态反馈控制律存在的充分条件,该条件同时保证闭环系统满足γ-次优H∞性能,为控制器的设计提供了理论依据。
2.
Applying a stabilizing state feedback control to systemsm,aking use of the Lyapunov-Krasovskii functional and combining the method of the linear matrix inequalities(LMI)t,he sufficient conditions of robust BIBO stabilization for T-S fuzzy control systems are obtained.
研究了一类具有不确定系数的T-S模糊控制系统的鲁棒BIBO镇定问题,应用稳定的状态反馈控制,通过构造Lyapunov-Krasovskii泛函,采用线性矩阵不等式(LMI)方法,给出了T-S模糊连续控制系统Robust有界输入有界输出镇定的充分条件;当参考的输入信号r(t)≡0时,给出了T-S模糊连续控制系统的零解鲁棒镇定的充分条件。
3.
Constructing a suitable Lyapunov-Krasovskii functional and based on the scheme of decentralized control,the design of a control law is proposed to ensure the global asymptotic synchronization of state trajectories of two chaotic neural networks of which the structure are the same and the initial conditions are different.
基于分散控制策略,通过构造适当的Lyapunov-Krasovskii泛函,给出了保证两个具有相同结构但初始条件不相同的时滞混沌神经网络全局渐近同步的控制律设计方法。
3) augmented Lyapunov-Krasovskii functional
增广Lyapunov-Krasovskii泛函
1.
Being different from existing reports,the novel delay-dependent robust stability criteria for interval recurrent neural networks with mixed time-varying delays employ a new augmented Lyapunov-Krasovskii functional.
与之前的处理方法不同,在本文中通过使用一种新型的增广Lyapunov-Krasovskii泛函,从而得到了一类新颖的关于区间递归神经网络的时滞依赖全局鲁棒稳定性判据。
4) fuzzy Lyapunov-Krasovskii functional
模糊Lyapunov-Krasovskii泛函
1.
Based on a fuzzy Lyapunov-Krasovskii functional(LKF) and fuzzy free-weighting matrices with time-delay,a new delay-dependent robust stability criterion is proposed and proved for the close-loop fuzzy system.
基于模糊Lyapunov-Krasovskii泛函(LKF),引入多个模糊时滞自由权值矩阵,提出并证明了闭环系统新的时滞相关鲁棒H∞渐近稳定的充分条件。
5) Lyapunov-Krasovskii functional based on "descriptor form"
基于"descriptor form"的Lyapunov-Krasovskii泛函
6) Lyapunov-Krasovskii functional based on descriptor form
基于descriptor form的Lyapunov-Krasovskii泛函
补充资料：Марков过程的泛函
Марков过程的泛函
functional of a Markov process
M仰助“过程的泛函【加犯份班司健a扮如d如vpr以犯岛;中y业，o.a月oT Map二招e.o np()朋eCea] 一个以可测方式依赖于MaPKo.过程轨道的随机变量或随机函数，其可测性条件随具体情况而定.在MaP盆oB过程的一般理论中，采用以下的泛函定义.假设给定一个具有时间推移算子氏的非停止齐次M叩-Ko。过程(M田玉ov plx兀启弥)X二(xr，风，氏)，其相空间为可测空间(In纷s幽 blespaCe)LE，少)，设才是基本事件空间中包含每个形如{。:x，“B}(t)0，B任分)的事件的最小。代数，/’是对于所有可能的测度Px(x‘E)关于/’的完全化的交.如果对于每个t)O，7，关于。代数才门不是可测的，那么，称随机函数叭(‘)0)为Ma伴oB尽捍X的攀甲(丘功d沁n目of此MaJ改ov Pnx君邓)· 人们特别关心的是M川阵..过程的乘性和加性泛函.它们分别润足条件下，十:，下;疏凡和，，十，，，，+氏大，s，亡》0.这里假定，，在【0，co)上是右连续的(代替这些条件，有时只假定对所有固定的s，t)O，这些条件关于P:几乎处处成立).在停止和非齐次过程的情形下，采用类似的方式来定义.MaPI..过程x‘(x，，心，不，P)的加性泛函的例子可以通过以下方式得到:设对于t<‘，，，等于f(x，)一f(x。)，或北f(气)d:，或随机函数f(x，)在:。10，，]中跳跃值的和，这里f(x)是有界并且关于岁可侧的函数(第二和第三个例子只在某些附加限制下有效).从任意加性泛函，.，可以得到乘性泛函以py，.在标准MaP-血过程的情况下，设t
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条