一类离散非线性不确定时滞系统的鲁棒滑模滤波

This paper is concerned with the problem of robust sliding-mode filtering for a class of uncertain nonlinear discrete-time systems with time-delays. The nonlinearities are assumed to satisfy global Lipschitz conditions and parameter uncertainties are supposed to reside in a polytope. The resulting filter is of the Luenberger type with the discontinuous form. A sufficient condition with delay-dependency is proposed for existence of such a filter. And the desired filter can be found by solving a set of matrix inequalities. The resulting filter adapts for the systems whose noise input is real functional bounded and not be required to be energy bounded. A numerical example is given to illustrate the effectiveness of the proposed design method.     

Fuzzy Particle Filtering for Uncertain Systems

In this paper, we propose a novel fuzzy particle filtering method for online estimation of nonlinear dynamic systems with fuzzy uncertainties. This approach uses a sequential fuzzy simulation to approximate the possibilities of the state intervals in the state-space, and estimates the state by fuzzy expected value operator. To solve the degeneracy problem of the fuzzy particle filter, one corresponding resampling technique is introduced. In addition, we compare the fuzzy particle filter with ordinary particle filter in both aspects of the theoretical basis and algorithm design, and demonstrate that the proposed filter outperforms standard particle filters especially when the number of the particles is small. The numerical simulations of two continuous-state nonlinear systems and a jump Markov system are employed to show the effectiveness and robustness of the proposed fuzzy particle filter.     

不确定离散系统的最优鲁棒滤波

本文对一类含有范数有界参数不确定的离散线性系统的滤波问题进行了研究,了有限时域时变以及无限时域时不变两种情形,给出了一个对所有可容许参数不确定都能满足的估计误差方差上界,得到了使得该上界达到最小的最优鲁棒滤波器形式及其存在的充要条件,数值结果表明：当系统存在参数不确定时,本文所得到的滤波器优于标准的Ｋａｌｍａｎ滤波器以及文（４）中的鲁棒滤波器。     

A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems

In this paper, a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems. Motivated by the classical on-policy and off-policy algorithms of reinforcement learning, the online feedback relearning (FR) algorithm is developed where the collected data includes the influence of disturbance signals. The FR algorithm has better adaptability to environmental changes (such as the control channel disturbances) compared with the off-policy algorithm, and has higher computational efficiency and better convergence performance compared with the on-policy algorithm. Data processing based on experience replay technology is used for great data efficiency and convergence stability. Simulation experiments are presented to illustrate convergence stability, optimality and algorithmic performance of FR algorithm by comparison.      

一类非线性不确定系统的鲁棒观测器设计

对于一类非线性不确定系统,给出一种基于观测器的鲁棒稳定控制器设计的新方法,它适用于一般匹配不确定系统,且对全维和降维两种观测器均进行了研究.设计实例表明,所设计的控制器反馈增益幅值较小、实现方便.     

Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings

In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.      

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.      

非线性系统的自适应推广的kalman滤波

本文提出了未知噪声统计的非线性系统中新的自适应推广的Kalman滤波算法.作者提出了用虚拟时变噪声统计[1,2],补偿线性化模型误差的新思想.在本文中,作者指出了文献[3]中,用Sage和Husa的常值噪声统计估值器来估计虚拟噪声是不合理的.另外,即使原非线性系统的噪声统计是零均值,但线性化的模型的噪声统计一般是非零均值的.两个数值模拟例子说明了本文方法的有效性.     

不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.     

A Simple Nonlinear Observer for a Class of Uncertain Mechanical Systems

A simple nonlinear observer is proposed for a class of uncertain nonlinear multiple-input-multiple-output (MIMO) mechanical systems whose dynamics are first-order differentiable. The global asymptotic observation of the proposed observer is proved. Thus, the observer can be designed independently of the controller. Furthermore, the proposed observer is formulated without any detailed model knowledge of the system. These advantages make it easy to implement. Numerical simulations are included to illustrate the effectiveness of the proposed observer.     

Evolutionary-Programming-Based Kalman Filter for Discrete-Time Nonlinear Uncertain Systems

Some observations and improvements on the conventional Kalman filtering scheme to function properly are presented. The improvements can be achieved using the minimal principle evolutionary programming (EP) technique. A new linearization methodology is presented to obtain the exact linear models of a class of discrete-time nonlinear time-invariant systems at operating states of interest, so that the conventional Kalman filter can work for the nonlinear stochastic systems. Furthermore, a Kalman innovation filtering algorithm and such an algorithm based on the evolutionary programming optimal-search technique are proposed in this paper for discrete-time time-invariant nonlinear stochastic systems with unknown-but-bounded plant uncertainties and noise uncertainties to find a practically implementable “best” Kalman filter. The worst-case realization of the discrete-time nonlinear stochastic uncertain systems represented by the interval form with respect to the implemented “best” nominal filter is also found in this paper for demonstrating the effectiveness of the proposed filtering scheme.     

Delay-Independent Stability Conditions for Time-Varying Nonlinear Uncertain Systems

A new stability criterion for time-varying systems consisting of linear and norm bounded nonlinear terms with uncertain time-varying delays is formulated. An explicit delay-independent sufficient stability condition is formulated in the terms of the transition matrix of the given linear part without delay and the bounds for the uncertain terms. The obtained condition turns out to be also necessary if the matrix of the linear part is time-invariant and symmetric; it is shown that these systems satisfy the well-known Aizerman's conjecture. The obtained criterion is contrasted by some of stability estimates available in the literature for these kinds of systems; in all cases the proposed criterion provides less conservative stability bounds.     

A Unified Adaptive Iterative Learning Control Framework for Uncertain Nonlinear Systems

In this note, we propose a unified framework for adaptive iterative learning control design for uncertain nonlinear systems. It is shown that if a Lyapunov based adaptive control law is available for the system under consideration and the Lyapunov function satisfies certain conditions, it is straightforward to extend the adaptive controller to handle repetitive systems operating over a finite time interval. According to the value of a certain parameter gamma, the parametric adaptation law can be a pure time-domain adaptation, a pure iteration-domain adaptation or a combination of both.A pure iteration-domain adaptation is described by a difference equation, a pure time-domain adaptation is described by a differential equation, and a combination of both is described by a differential-difference equation. The advantages and disadvantages of the three possible adaptation types are discussed and some illustrative examples are given. [All rights reserved Elsevier].     

不确定时滞系统的非线性保性能控制

针对一类非线性系统和给定的性能指标,研究其保性能控制问题。基于Lyapunov稳定性理论,采用线性矩阵不等式工具,给出非线性状态反馈保性能控制器存在的一个充分条件,并依据其可行解给出相应保性能控制律的设计方法。建立一个具有线性矩阵不等式约束的凸优化问题,得到非线性系统的最优保性能控制律。仿真示例表明该方法的可行性。     

不确定非线性系统的时滞依赖保性能控制

针对一类基于T-S模糊模型表示的具有时变状态时滞和范数有界不确定性非线性系统,研究了时滞依赖保性能模糊控制器设计问题。对于用T-S模糊模型表示的非线性时滞系统,已知系统的时变时滞本身及其变化率的上界,采用并行分散补偿技术,通过选取合适的Lyapunov函数,推导了依赖时滞上界及变化率上界的时滞保性能模糊控制器存在的充分条件,进而通过建立和求解LMI（线性矩阵不等式）约束的凸优化问题,给出了保性能控制律的设计方法。数值算例表明了该方法的有效性。     

基于状态观测器的一类非线性系统的模糊鲁棒控制

利用T－S模型对一类非线性不确定系统进行模糊建模,在此基础上研究模糊鲁棒观测器及模糊状态鲁棒控制器的设计,并证明所设计的模糊鲁棒观测器和模糊状态鲁棒控制器具有全局渐近稳定性质。     

不确定非线性系统的非光滑控制及应用

针对一类具有严格反馈结构形式的不确定非线性系统,研究非光滑鲁棒控制问题。在一定的假设条件下,基于backstepping设计方法,设计鲁棒非光滑控制律,证明闭环系统的鲁棒稳定性。将所得结果应用于飞行器的末制导问题,设计非光滑末制导律,并进行数值仿真研究。仿真结果说明了方法的有效性。