Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper, a new filtering fusion problem is studied for nonlinear cyber-physical systems under error-variance constraints and denial-of-service attacks. To prevent data collision and reduce communication cost, the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors. Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identically-distributed random variables. From the defenders’ view, the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence. At the local filtering stage, a set of variance-constrained local filters are designed where the upper bounds (on the filtering error covariances) are first acquired and later minimized by appropriately designing filter parameters. At the fusion stage, all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule. Furthermore, the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance. A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.      

支持SRP协议的实时调度技术

实时操作系统对多任务资源访问控制提出了较高的要求.资源竞争常会引起优先级翻转问题导致任务阻塞,增加资源等待延迟时间.传统资源访问控制协议可以部分解决此问题,但是存在上下文切换次数高,任务调度效率低等不足.在结合SRP协议的提前阻塞特性和MiniCore实时调度器设计后,分析了协议中任务调度规则,指出了调度器支持SRP协议时效率低下的原因,并引入胜者树结构改进就绪队列,给出了新的任务搜索算法.理论分析与实验结果表明改进的调度器在任务集规模较大时,提高了调度效率,较好的支持了SRP协议.     

Adaptive Event-triggered Control for Stochastic Nonlinear Multi-agent Systems with Unknown Control Directions

This paper focuses on the adaptive event-triggered consensus control problem for a class of stochastic nonlinear multi-agent systems with unknown nonlinear control directions and external disturbances. The heterogeneous nonlinear dynamics and non-identical unknown control directions are discussed for different agents. The application of the event-triggered mechanism can effectively decrease the update frequency of the controller, and unknown nonlinear dynamics are solved by using fuzzy logic systems. Under the action of the designed distributed controller, all signals of this stochastic multi-agent systems can reach semi-globally uniformly ultimately bounded (SGUUB) in mean square. Furthermore, Zeno behavior can be ruled out by the existence of positive inter-event intervals. Finally, a simulation example is presented to verify the feasibility of the algorithm.

     

非线性离散随机时变系统比例微分滤波

扩展卡尔曼滤波（EKF）是从极小化状态估计误差的方差得到的，没有考虑状态误差的变化率，因而对非线性时变系统EKF估计方法惯性作用 较大，从而产生估计滞后，提出了非线性离散随机系统比例微分滤波（PDF），PDF联合考虑极小化状态估计误差和状态误差变化率的方差，克服了EKF对非线性时变系统估计滞后的缺点，估计具有适时性，提出了高估计的精度，仿真例子证明了所提出的估计方法的有效性。     

虚拟控制增益函数未知的随机非线性系统模糊自适应控制

The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.     

Entropy Optimization Filtering for Fault Isolation of Nonlinear Non-Gaussian Stochastic Systems

In this paper, the fault isolation (FI) problem is investigated for nonlinear non-Gaussian systems with multiple faults(or abrupt changes of system parameters) in the presence of noises. By constructing a filter to estimate the states, the FI problem can be reduced to an entropy optimization problem subjected to the non-Gaussian estimation error systems. The design objective for the FI purpose is that the entropy of the estimation error is maximized in the presence of diagnosed fault and is minimized in the presence of the nuisance faults or noises. It is shown that the error dynamics is represented by a nonlinear non-Gaussian stochastic system, for which new relationships are applied to formulate the probability density functions (PDFs) of the stochastic error in terms of the PDFs of the noises and the faults. The Renyi's entropy has been used to simplify the computations in the filtering for the recursive design algorithms. It is noted that the output can be supposed to be immeasurable (but with known stochastic distributions), which is different from the existing results where the output is always measurable for feedback. Finally, simulations are given to demonstrate the effectiveness of the proposed data-driven FI filtering algorithms.     

零知识下的比特流未知协议分类模型

针对在零知识下识别比特流未知协议这一问题,提出了一种协议分类模型。该模型首先利用二进制流的固有特性来计算协议种类个数近似值K和初始聚类中心,然后使用改进的K-Means聚类算法指定K及初始聚类中心以进行聚类,最后使用基于信息熵的混杂度评价方法对聚类结果进行评价,可将评价结果较好的类簇作为一种协议类型进行标记,用于其他分析。使用林肯实验室发布的实验数据进行测试,结果表明该模型能以较高的准确率对未知协议进行分类,基于信息熵的类簇评价方法也具有一定实用性。     

Adaptive Output Feedback Control for Switched Stochastic Nonlinear Systems with Time-varying Parameters and Unknown Output Functions

International Journal of Control, Automation and Systems - In this paper, we discuss the adaptive output feedback control problem for switched stochastic nonlinear systems which involve uncertain...     

Cost-Based Filtering Techniques for Stochastic Inventory Control Under Service Level Constraints

This paper¹ considers a single product and a single stocking location production/inventory control problem given a non-stationary stochastic demand. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using three cost-based filtering methods. Our approach can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all. This work was supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications Value-Chain-Driven Research (CTVR) and Grant No. 00/PI.1/C075. ¹This paper is an extended version of [19].     

Event-triggered Fusion Estimation for Nonlinear Stochastic System with Unknown Parameters and Transmission Fading Over Sensor Networks

International Journal of Control, Automation and Systems - This paper investigates an event-triggered fusion estimation problem for a class of discrete-time nonlinear stochastic systems with...     

未知探测概率的自适应多目标跟踪算法

为在复杂背景下对系统探测概率进行精确建模,提出一种适用于探测概率未知情形的多目标跟踪算法。通过时变自回归过程对探测概率进行建模,将参数化模型与标签多伯努利(LMB)滤波器相结合,并给出算法的序贯蒙特卡洛实现。仿真结果表明,所提算法的目标数和目标状态估计结果均优于Beta势平衡多目标多伯努利算法,平均最优次模型分配距离明显小于固定探测概率的LMB算法。     

Strategy Under the Unknown Stochastic Environment: the Nonparametric Lob—Pass Problem

The bandit problem consists of two factors, one being exploration or the collection of information on the environment and the other being the exploitation or taking benefit by choosing the optimal action in the uncertain environment. It is desirable to choose only the optimal action for the exploitation, while the exploration or collection of information requires taking a variety of (nonoptimal) actions as trials. Hence, in order to obtain the maximal cumulative gain, we need to compromise between the exploration and exploitation processes. We treat a situation where our actions change the structure of the environment, of which a simple example is formulated as the lob—pass problem by Abe and Takeuchi. Usually, the environment is specified by a finite number of unknown parameters in the bandit problem, so that the information collection part is to estimate their true values. This paper treats a more realistic situation of nonparametric estimation of the environment structure which includes an infinite number (a functional degree) of unknown parameters. A strategy is given under such a circumstance, proving that the cumulative regret can be made of the order O(log t) , O((log t) ² ) , or O(t ^1-σ ) (0< σ <1) depending on the dynamics of the environment, where t is the number of trials, in contrast with the optimal order O(log t) in the parametric case. Received December 14, 1996; revised June 14, 1997, and July 24, 1997.     

非线性随机系统的概率密度追踪控制

针对目前非线性随机系统控制方法的设计复杂、 计算成本高以及缺乏稳定性或收敛性证明等缺点, 提出了一种全新的基于等效非线性系统法求近似稳态解的思想设计的非线性随机系统的反馈控制, 使受控系统输出的稳态概率密度函数逼近事先给定的目标概率密度函数. 利用 Lyapunov 函数法证明了受控系统的收敛性. 数学仿真结果证明了这种方法的可行性和正确性.     

NN‐Based Output‐Feedback Control for Stochastic Nonlinear Systems with Unknown Control Directions

This paper addresses the neural network‐based output‐feedback control problem for a class of stochastic nonlinear systems with unknown control directions. The restrictions on the drift and diffusion terms are removed and the conditions on unknown control directions are relaxed. By introducing a proper coordinate transformation, and combining dynamic surface control (DSC) technique with radial basis function neural network (RBF NN) approximation approach, we construct an adaptive output‐feedback controller to guarantee the closed‐loop system to be mean square semi‐globally uniformly ultimately bounded (M‐SGUUB). A simulation example demonstrates the effectiveness of the proposed scheme.     

The Conditionally Minimax Nonlinear Filtering Method and Modern Approaches to State Estimation in Nonlinear Stochastic Systems

We consider, in chronological order, the main results that have defined the concept of conditionally minimax nonlinear filtering. This would let us to follow all the evolution stages of this universal method, from a particular application, through basic mathematical concepts, to an advanced theory able to solve a wide class of robust estimation problems in linear and nonlinear stochastic systems.     

VxWorks下的分布式系统中StarFabric通信协议

为了解决分布式系统中主要采用的以太网互联中高延时、低带宽等问题,采用StarFabric高速总线互联,设计了一个高效的StarFabric传输应用层协议,并在VxWorks操作系统中实现了这个协议的编程接口。经测试StarFabric延迟远低于以太网,带宽远高于以太网,从而证明设计是成功的。     

一类虚拟控制系数未知的随机非线性时滞大系统的适应镇定控制

研究了一类具有未知虚拟控制系数和未知噪声协方差的随机非线性时滞大系统的适应镇定问题. 首先, 针对系统的未知虚拟控制系数和未知噪声协方差, 选取了相应的估计参数; 然后, 针对时变时滞对闭环系统稳定性的影响, 构造了适当形式的Lyapunov-Krasovskii泛函, 采用积分反推方法给出了无记忆状态反馈控制律的系统设计过程. 在一定条件下, 证明了闭环系统平衡点依概率全局稳定, 且除参数估计以外的所有闭环信号几乎均收敛到零点. 仿真算例验证了所给方法的有效性.