线性不确定时滞系统指定衰减度鲁棒镇定

研究了一类线性不确定时滞系统时滞依赖型具有指定衰减度的无记忆状态反馈鲁 棒镇定问题.所考虑的线性不确定时滞系统含有时变未知但有界的不确定参数和状态滞后. 通过应用Razumikhin定理和Lyapunov定理,导出了系统鲁棒稳定且具有指定衰减度的判 据和具有指定衰减度的无记忆状态反馈鲁棒镇定控制律存在的充分条件及相应的控制器设 计方法.所得时滞相关的结果用一组线性矩阵不等式(LMI)表示.     

不确定时变时滞切换广义系统的鲁棒H∞ 保性能控制

利用公共 Lyapunov 泛函方法和凸组合技术研究了一类不确定时变时滞切换广义系统的鲁棒 ∞保性能控制和状态反馈镇定问题.在设定的切换规则下,给出了基于线性矩阵不等式表示的鲁棒 ∞保性能控制器存在的充分条件,保证了系统具有鲁棒 ∞干扰抑制水平 及状态反馈可切换镇定的同时满足保性能指标.最后的仿真示例验证了该方法的有效性.     

一类不确定随机切换系统的镇定性和H∞性能研究

针对一类不确定随机切换系统,利用随机李亚普诺大稳定性理论和伊藤微分法则,研究了该系统在一定切换条件下鲁棒镇定和鲁棒H∞控制器存在的充分条件.使用状态反馈技术所设计的无记忆控制器能在所有容许不确定下保证闭环系统渐近稳定.文中的研究结果以线性矩阵不等式的形式给出,算例和仿真表明文中控制器设计方法的正确性和有效性.     

一类含有输入时滞的不确定切换系统的鲁棒指数镇定

针对一类含有输入时滞的不确定切换系统,讨论了鲁棒指数镇定问题,提出了可行的切换控制器和切换信号的设计方法．首先,运用还原法将具有时滞的切换系统转化为无时滞的切换系统;然后,基于线性矩阵不等式,给出了系统在切换控制器和切换信号作用下鲁棒指数可镇定的充分条件,并利用多Ｌｙａｐｕｎｏｖ函数方法给出了定理的详细证明;最后,利用数值仿真例子验证了该方法的有效性．     

一类离散时间切换混杂系统鲁棒控制

由于切换规则的存在使得切换混杂控制系统的稳定性研究变得极为复杂,如何针对给定的系统设计适当的控制器和切换规则没有统一的方法.本文考虑一类线性不确定离散时间切换混杂系统的鲁棒二次镇定和渐近镇定问题.利用公共李雅普诺夫函数方法和多李雅普诺夫函数方法,分别设计了切换混杂系统鲁棒状态反馈控制器和鲁棒输出反馈控制器,保证了切换混杂系统的二次稳定性和渐近稳定性.仿真结果验证了所提算法的正确有效性.     

线性不确定多时滞系统的α-鲁棒控制

通过分析时滞系统超越型的特征方程的根的分布,并结合线性矩阵不等式(LMI)技术研究了含多个不确定常时滞的线性不确定时滞系统的可α-鲁棒镇定及其控制器设计问题,得到了相应的α-鲁棒无记忆反馈控制律.不同于一般的结果,本方法得到的控制器不但使得系统可鲁棒镇定,而且闭环系统特征方程的根的实部均小于等于某个指定的负数.结果表示为LMI形式,易于进行数值处理.最后以一个数值例子显示了所得结果的有效性及其应用方法.     

多模态It随机系统的均方稳定性与鲁棒镇定

研究由连续时间Markov链所确定的多模态It^o随机系统的均方稳定性与鲁棒镇定 ,得到了一般多模态It^o随机系统的k阶矩指数稳定性定理 ,线性不确定系统的均方稳定性定理 ,给出了线性不确定系统的鲁棒镇定控制器 .     

多模态It(o)随机系统的均方稳定性与鲁棒镇定

研究由连续时间Markov链所确定的多模态It?随机系统的均方稳定性与鲁棒镇定,得到了一般多模态It?随机系统的k阶矩指数稳定性定理,线性不确定系统的均方稳定性定理,给出了线性不确定系统的鲁棒镇定控制器.     

不确定关联大系统对时变参数的自适应控制

考虑具有时滞的不确定非线性关联大系统的鲁棒控制问题．假设不确定时变参数为半线性或非线性系统的有界输出，通过对时变不确定参数设计自适应律，从而对不确定参数进行估计．利用线性矩阵不等式技术和自适应参数估计方法，设计出鲁棒自适应控制器，从而保证闭环系统渐近稳定．建立了可由线性矩阵不等式表示的镇定条件．仿真示例说明该方法是有效的．     

一类线性时滞系统鲁棒控制器的设计

针对一类同时存在状态和控制滞后的线性不确定，时滞系统的鲁棒镇定问题进行了研究，其中系统的不确定项参数时变未知但范数有界、且滞后项也是时变的。对此，基于Lyapunov泛函，利用线性矩阵不等式给出了系统可由状态反馈鲁棒镇定的充分条件，并且利用线性矩阵不等式的解构造了使得系统鲁棒稳定的无记忆状态反馈控制律，所得结果是依赖时滞大小的，且与时滞的导数有关，从而相对减弱了控制器设计的保守性，最后通过一个例子说明了方法的有效性。     

Almost Sure Stabilization for Feedback Controls of Regime-Switching Linear Systems With a Hidden Markov Chain

This work is devoted to the almost sure stabilization of adaptive control systems that involve an unknown Markov chain. The control system displays continuous dynamics represented by differential equations and discrete events given by a hidden Markov chain. In the previous investigation on this class of problems, averaging criteria were used, which provides only the system behavior in some expectation sense. A closer scrutiny of the system behavior necessarily requires the consideration of sample path properties. Different from previous work on stabilization of adaptive controlled systems with a hidden Markov chain, where average criteria were considered, this work focuses on the almost sure stabilization or sample path stabilization of the underlying processes. Under simple conditions, it is shown that as long as the feedback controls have linear growth in the continuous component, the resulting process is regular. Moreover, by appropriate choice of the Lyapunov functions, it is shown that the adaptive system is stabilizable almost surely. As a by-product, it is also established that the controlled process is positive recurrent.     

Discrete-Time Adaptive Command Following and Disturbance Rejection With Unknown Exogenous Dynamics

We present an adaptive controller that requires limited model information for stabilization, command following, and disturbance rejection for mult-input multi-output minimum-phase discrete-time systems. Specifically, the controller requires knowledge of the open-loop system's relative degree as well as a bound on the first nonzero Markov parameter. Notably, the controller does not require knowledge of the command or the disturbance spectrum as long as the command and disturbance signals are generated by a Lyapunov-stable linear system. Thus, the command and disturbance signals are combinations of discrete-time sinusoids and steps. In addition, the Markov-parameter-based adaptive controller uses feedback action only, and thus does not require a direct measurement of the command or disturbance signals. Using a logarithmic Lyapunov function, we prove global asymptotic convergence for command following and disturbance rejection as well as Lyapunov stability of the adaptive system when the open-loop system is asymptotically stable.      

Markov Chains, Entropy, and Fundamental Limitations in Nonlinear Stabilization

In this paper, we propose a novel methodology for establishing fundamental limitations in nonlinear stabilization. To aid the analysis, we express the stabilization problem as control of Markov chains. Using Markov chains, we derive the limitations as certain maximum probability bounds or as positive conditional entropy of the certain signals in the feedback loop. The former is related to the infeasibility of the asymptotic stabilization in the presence of quantization and the latter to the Bode integral formula. In either cases, it is shown that uncertainty - associated here with the unstable eigenvalues of the linearization - leads to fundamental limitations.     

Adaptive control of linear Markov jump systems

Stabilization of linear Markov jump systems via adaptive control is considered in this paper. The switching law is assumed to be unobservable Markov process. A sufficient condition is obtained for the stochastic stabilizability based on common quadratic Lyapunov functions (QLFs). The constructive proof provides a method to construct a sampling adaptive stabilizer. An example is used to describe the design of adaptive control, which stabilizes the system.     

转移概率部分未知的随机Markov 跳跃系统的镇定控制

研究一类随机Markov跳跃系统的稳定性与镇定控制问题.此类系统跳跃过程的转移概率部分未知,包括转移概率完全已知和完全未知两种情形,因而更具一般性.首先,给出保证随机Markov跳跃系统均方渐近稳定的充分性判据,并设计了相应的状态反馈镇定控制器;然后,基于矩阵的奇异值分解给出了系统静态输出反馈镇定控制器的设计方法,并将其归结为求解一组线性矩阵不等式（LMIs）的可行性问题;最后,通过数值仿真验证了所得结论的正确性.     

Adaptive estimation of doubly stochastic poisson processes

The optimal adaptive estimator structures for a class of doubly stochastic Poisson processes (DSPP) are presented. The structure is used along with a moment assumption to obtain implementable estimators. The class of DSPP considered is that of a linear Markov diffusion process modulating a linear intensity rate. The uncertainty for which the adaptation process is developed includes both structures uncertainty in the Markov diffusion process and parameter uncertainty in the Markov diffusion process and the intensity rate process. Results are given on the problem of adaptation of which of a finite number of Markov realizations is modulating the intensity process. The nonlinear adaptive estimator structures are obtained by use of a particular theorem that yields an optimal structure for the adaptive estimator. The structure is used to obtain a quasi-optimal adaptive estimator for the problem by use of a zero third central moment assumption. The estimator structure consists of a nonlinear, nonadaptive part, and a nonlinear, adaptive part which contains the parameter structure adaptations. The necessary covariance equations for performance evaluation are obtained. The theory is applied to the problem of wavefront estimation in adaptive optics for use in high-energy lasers and in imaging through atomospheric turbulence. Other examples are given.     

一类非线性不确定系统的最优自适应控制

研究了一类含有系统扰动，并且状态项与控制项中同时含有未知参数的非线性系统的反馈稳定问题.在控制器的设计中，将原系统的自适应稳定问题转化为扩展系统的非自适应稳定问题，并利用扩展系统的鲁棒控制Lyapunov函数，设计出使原系统自适应稳定的控制律.进一步，利用逆最优的方法，证明了该控制律同时也是满足某种性能指标的最优控制。     

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

This paper introduces an adaptive visual tracking method that combines the adaptive appearance model and the optimization capability of the Markov decision process. Most tracking algorithms are limited due to variations in object appearance from changes in illumination, viewing angle, object scale, and object shape. This paper is motivated by the fact that tracking performance degradation is caused not only by changes in object appearance but also by the inflexible controls of tracker parameters. To the best of our knowledge, optimization of tracker parameters has not been thoroughly investigated, even though it critically influences tracking performance. The challenge is to equip an adaptive tracking algorithm with an optimization capability for a more flexible and robust appearance model. In this paper, the Markov decision process, which has been applied successfully in many dynamic systems, is employed to optimize an adaptive appearance model-based tracking algorithm. The adaptive visual tracking is formulated as a Markov decision process based dynamic parameter optimization problem with uncertain and incomplete information. The high computation requirements of the Markov decision process formulation are solved by the proposed prioritized Q-learning approach. We carried out extensive experiments using realistic video sets, and achieved very encouraging and competitive results.     

Robust finite-time <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> control of stochastic jump systems

This paper provides the stochastic finite-time stabilization and H _∞ control problem of Markov jump systems with norm-bounded uncertainties and state delays that possess randomly jumping parameters. The transition of the jumping parameters is governed by a finite-state Markov process. The finite-time H _∞ controller via state feedback is provided to guarantee the stochastic finite-time bounded-ness and stochastic finite-time stabilization of the resulting closed-loop system for all admissible uncertainties and unknown time-delays. The control criterion is formulated in the form of linear matrix inequalities and the designed finite-time stabilization controller is described as an optimization one. Simulation results illustrate the effectiveness of the developed approaches.     

Adaptive robust simultaneous stabilization of multiple n-degree-of-freedom robot systems

In this paper, the adaptive robust simultaneous stabilization problem of uncertain multiple n-degree-of-freedom (n-DOF) robot systems is studied using the Hamiltonian function method, and the corresponding adaptive L2 controller is designed. First, we investigate the adaptive simultaneous stabilization problem of uncertain multiple n-DOF robot systems without external disturbance. Namely, the single uncertain n-DOF robot system is transformed into an equivalent Hamiltonian form using the unified partial derivative operator (UP-DO) and potential energy shaping method, and then a high dimensional Hamiltonian system for multiple uncertain robot systems is obtained by applying augmented dimension technology, and a single output feedback controller is designed to ensure the simultaneous stabilization for the higher dimensional Hamiltonian system. On this basis, we further study the adaptive robust simultaneous stabilization control problem for the uncertain multiple n-DOF robot systems with external disturbances, and design an adaptive robust simultaneous stabilization controller. Finally, the simulation results show that the adaptive robust simultaneous stabilization controller designed in this paper is very effective in stabilizing multi-robot systems at the same time.