一类时滞非线性系统的自适应模糊控制

对于一类SISO输入时滞已知,状态时滞不确定但有上界的能采用后推设计方法的非线性系统提出一种基于后推设计、自适应模糊控制和滑模控制的控制方案.通过状态变换,把输入时滞系统转化为无输入时滞的系统.用模糊系统来估计系统的未知连续函数,对转化后的新系统设计自适应滑模控制器,使得新系统的状态有界,通过递推证得原系统的状态半全局一致有界.     

一种基于神经元的自适应模糊推理网络

本文根据Ｔ－Ｓ模糊模型提出了一种新的基于神经元的自适应模糊推理网络，给出了连接结构和学习算法，它能自动学习和修正隶属函数及模糊规则，将其用于Ｂｏｘ的煤气炉，太阳黑子预报以及降雨量预报等不同类型的复杂系统建模，仿真结果表明，该模糊神经网络具有收敛速度快，辨识精度高，泛化能力强和适应范围广等特点，可当作复杂系统建模的一种有效工具。     

Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal

Container terminals pay more and more attention to the service quality of inland transport modes such as tucks, trains and barges. Truck appointment systems are a common approach to reduce truck turnaround times. This paper provides a tool to use the truck appointment system to increase not only the service quality of trucks, but also of trains, barges and vessels. We propose a mixed integer linear programming model to determine the number of appointments to offer with regard to the overall workload and the available handling capacity. The model is based on a network flow representation of the terminal and aims to minimize overall delays at the terminal. It simultaneously determines the number of truck appointments to offer and allocates straddle carriers to different transport modes. Numerical experiments, conducted on actual data, quantify the benefits of this combined solution approach. Discrete-event simulation validates the results obtained by the optimization model in a stochastic environment.     

Identification of nonlinear system using extreme learning machine based Hammerstein model

In this paper, a new method for nonlinear system identification via extreme learning machine neural network based Hammerstein model (ELM-Hammerstein) is proposed. The ELM-Hammerstein model consists of static ELM neural network followed by a linear dynamic subsystem. The identification of nonlinear system is achieved by determining the structure of ELM-Hammerstein model and estimating its parameters. Lipschitz quotient criterion is adopted to determine the structure of ELM-Hammerstein model from input–output data. A generalized ELM algorithm is proposed to estimate the parameters of ELM-Hammerstein model, where the parameters of linear dynamic part and the output weights of ELM neural network are estimated simultaneously. The proposed method can obtain more accurate identification results with less computation complexity. Three simulation examples demonstrate its effectiveness.     

Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems

In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme.     

Structure and logic in knowledge-based representations of systems simulation models

The paper focuses on the similarity between modelling and knowledge representation, trying to bring together the OR/Systems Science and the Artificial Intelligence views when referring to a computer system simulation, especially of the discrete-event or the network types. The models we consider are generalized activity networks with resources, including either models with a finite lifetime, such as project scheduling networks, or steady state models, such as queueing networks. By enhancing the structure of entities and states and the logic of transitions within a model specification, modularity is improved and one may adopt a more declarative approach. The relational and rule-based representation formalisms are a convenient choice for that purpose. Then, the use of knowledge bases both for the static (i.e. consultative) and the dynamic (i.e. experimental) study of the model turns up to be more natural. Moreover, the task of building an expert system for decision support on system analysis or synthesis becomes easier. The paper reports some original work in the above directions, using a logic programming approach and an associated specification methodology based on general systems concepts.     

输入通道有干扰多变量MRAC系统全局稳定化控制

对具有未建模动态且输入通道存在干扰的动态不确定多输入多输出(MIMO)模型参考自适应控制(MRAC) 系统，仅应用系统的输入输出量测数据给出了一种变结构模型跟踪控制器设计机制.通过辅 助信号和带有记忆功能的正规化信号,并适当选择控制器参数, 所提出的变结构控制 (VSC)能保证闭环系统的全局稳定性,且跟踪误差可调整到任意小.     

Vibration control of active vehicle suspension system using fuzzy logic algorithm

Vehicle suspension along with tires and steering linkages is designed for safe vehicle control and to be free of irritating vibrations. Therefore the suspension system designs are a compromise between ride softness and handing ability. However, this work is concerned with a theoretical investigation into the ride behavior of actively suspended vehicles. It is based on using fuzzy logic control (FLC) to implement a new sort of active suspension system. Comparisons between the behavior of active suspension system with FLC with those obtained from active systems with linear-quadratic regulator (LQR), ideal skyhook system and the conventional passive suspension systems. Results are introduced in such a way to predict the benefits that could be achieved from a fuzzy logic system over other competing systems. Furthermore, a controller is designed and made by using results of FLC system, theoretical inputs are used to examine the validity of this controller. Moreover, comparison between actual outputs from this controller with those obtained theoretically is made to judge the validity of the controller. The results indicate that the controller has a good capability in simulation of the theoretical model.     

Exact inversion of decomposable interval type-2 fuzzy logic systems

It has been demonstrated that type-2 fuzzy logic systems are much more powerful tools than ordinary (type-1) fuzzy logic systems to represent highly nonlinear and/or uncertain systems. As a consequence, type-2 fuzzy logic systems have been applied in various areas especially in control system design and modelling. In this study, an exact inversion methodology is developed for decomposable interval type-2 fuzzy logic system. In this context, the decomposition property is extended and generalized to interval type-2 fuzzy logic sets. Based on this property, the interval type-2 fuzzy logic system is decomposed into several interval type-2 fuzzy logic subsystems under a certain condition on the input space of the fuzzy logic system. Then, the analytical formulation of the inverse interval type-2 fuzzy logic subsystem output is explicitly driven for certain switching points of the Karnik–Mendel type reduction method. The proposed exact inversion methodology driven for the interval type-2 fuzzy logic subsystem is generalized to the overall interval type-2 fuzzy logic system via the decomposition property. In order to demonstrate the feasibility of the proposed methodology, a simulation study is given where the beneficial sides of the proposed exact inversion methodology are shown clearly.     

A propagation model with defensive measures for PLC-PC worms in industrial networks

Programmable logic controllers (PLCs) are important components of industrial control systems (ICSs), which are digital electronic devices with microprocessors and lack network security considerations. With the explosion of cyber-attacks on the ICS network, the concept of the worm known as PLC-PC worm that can spread between the Internet and ICS network has attracted a great deal of attention. In this paper, for the first time, we propose a propagation model to explore the spread behavior of PLC-PC worms across the PLC-PC coupled network. Theoretical analysis of the model shows the disease-free and endemic equilibriums of the network with defensive measures. And the stability of the model is examined by an epidemic threshold value. Several numerical and simulation experiments are presented to illustrate the analysis. Based on the results, some ideas for restraining the spread of PLC-PC worms or reducing the security threats of the ICS network are suggested.     

A network model for stemflow solute transport

While the role of stemflow in directing and concentrating water and nutrients at the tree base is rarely in dispute, its mathematical representation remains a subject of inquiry and research. A network model that seeks to estimate stemflow solute concentration and leaching is proposed. The model accommodates the physico-chemical properties of individual furrows embedded within the tree bark and their interconnections. The within-furrow equations for water and solute transport that include leaching are first developed and integrated along a rough-bark network topology to describe solute concentration and fluxes out of the network. The model is parameterized using published data on stemflow, field measurements of bark geometry, and laboratory experiments on bark leaching for potassium, magnesium, and calcium. The parameterization is intended to impose plausibility constraints and not to test model predictions at a particular site, a single event, or an individual experiment. The outflow concentration is then analyzed as a function of the network complexity that includes asymmetry in the lengths or subpaths connecting network nodes. For a symmetric network, an effective ’channel-flow’ analogy may be used to represent solute concentration at the outflow. However, as the asymmetry increases in subpath lengths, the efficiency of the bark network at moving solutes diminishes for the same rainfall input onto the stem. The network representation featured here is by no means offering a ’finality’ to the stemflow mathematical representation. It must be viewed as an embryonic step that opens up the possibility of using modern advances in network theories to link rainfall properties to stemflow water and solute input from a variety of tree species with differing bark microrelief configurations into the soil.     

Representing and Learning Distributions with the Aid of a Neural Network

This paper describes a method by which a neural network learns to fit a distribution to sample data. The neural network may be used to replace the input distributions required in a simulation or mathematical model and it allows random variates to be generated for subsequent use in the model. Results are given for several data sets which indicate the method is robust and can represent different families of continuous distributions. The neural network is a three-layer feed-forward network of size (1-3-3-1). This paper suggests that the method is an alternative approach to the problem of selection of suitable continuous distributions and random variate generation techniques for use in simulation and mathematical models.     

Discovering the dynamic behavior of unknown systems using fuzzy logic

To know the dynamic behavior of a system it is convenient to have a good dynamic model of it. However, in many cases it is not possible either because of its complexity or because of the lack of knowledge of the laws involved in its operation. In these cases, obtaining models from input–output data is shown as a highly effective technique. Specifically, intelligent modeling techniques have become important in recent years in this field. Among these techniques, fuzzy logic is especially interesting because it allows to incorporate to the model the knowledge that is possessed of the system, besides offering a more interpretable model than other techniques. A fuzzy model is, formally speaking, a mathematical model. Therefore, this model can be used to analyze the original system using known systems analysis techniques. In this paper a methodology for extract information from unknown systems using fuzzy logic is presented. More precisely, it is presented the exact linearization of a Takagi–Sugeno fuzzy model with no restrictions in use or distribution of its membership functions, as well as obtaining its equilibrium states, the study of its local behavior and the search for periodic orbits by the application of Poincaré.     

Adaptive type-2 fuzzy backstepping control of uncertain fractional-order nonlinear systems with unknown dead-zone

A new problem of adaptive type-2 fuzzy fractional control with pseudo-state observer for commensurate fractional order dynamic systems with dead-zone input nonlinearity is considered in presence of unmatched disturbances and model uncertainties; the control scheme is constructed by using the backstepping and adaptive technique. To avoid the complexity of backstepping design process, the dynamic surface control is used. Also, Interval type-2 Fuzzy logic systems (IT2FLS) are used to approximate the unknown nonlinear functions. By using the fractional adaptive backstepping, fractional control laws are constructed; this method is applied to a class of uncertain fractional-order nonlinear systems. In order to better control performance in reducing tracking error, the PSO algorithm is utilized for tuning the controller parameters. Stability of the system is proven by the Mittag–Leffler method. It is shown that the proposed controller guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the origin. The efficiency of the proposed method is illustrated with simulation examples.     

Using a Volterra system model to analyze nonlinear response in video-packet transmission over IP networks

This paper presents a Volterra system-based nonlinear analysis of video-packet transmission over IP networks. With the Volterra system, which is applicable to the modeling of nonlinear dynamic systems from sets of input and output data, we applied a time-series analysis of measured data for network response evaluation. In a test-bed connected to the Internet, we measured two parameters: the time intervals between consecutive packets from a video server at the originating side, and the transmission time of packets between originating and terminating sides. We used these as input and output data for the Volterra system and confirmed that the relative error of this model changed with conditions of network systems, which suggested that the packet transmission process affected the degree of nonlinearity of the system. The proposed method can reproduce the time-series responses observed in video-packet transmission over the Internet, reflecting nonlinear dynamic behaviors such that the obtained results provided us with an effective depiction of network conditions at different times.     

Queueing analysis for input regulation method employing periodic monitoring and control

Congestion control schemes are provided in telecommunications systems to prevent overload. This paper studies a queueing model for one of the control schemes and an input regulation method employing hysteresis control. In the method, excess traffic is regulated at the entrance to the network to keep the network load below capacity in order to avoid throughput degradation.Many studies have been made on the queueing models with hysteresis control employing continuous time monitoring. However, in real systems, input control activation is usually determined by the information obtained by monitoring such factors as resource utilization and the number of lost calls, at regular intervals. Input regulation models employing monitoring at regular intervals received little attention. The present paper assumes that the monitoring interval is arbitrarily distributed. Steady state probabilities at monitoring time points and those at arbitrary time points are obtained by using the piecewise Markov process theory. Some perfomance measures are calculated from these probabilities and numerical examples are given for the cases of regular interval monitoring, exponentially distributed interval monitoring and continuous time monitoring.