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The paper deals with active fault diagnosis of stochastic large-scale systems consisting of several subsystems with separate inputs and observations, which are coupled through the system state. The subsystems are described by multiple models expressing their fault-free and faulty behaviour. The transition between the models is governed by a Markov chain. The paper proposes a distributed design of an active fault diagnosis algorithm, which takes into account the coupling among the subsystems in all stages of the algorithm. This results in a higher quality of the excitation signal and consequently in better decisions. The numerical example shows the improved performance of the proposed algorithm in comparison with the algorithms based on the decentralised design.

This paper investigates the containment control problem of multi-agent systems with semi-Markovian switching topologies. Different from the related works, this paper considers a more general case t...

This paper researches the stability and stabilisation problems of stochastic neural network with multiple time delays. As for time delay, neuron state delay is first introduced in this paper. The n...

This paper is motivated by a need to address the challenge of securing a safe landing after suffering from inflight impairment. In this paper, a new adaptive generalised model predictive static pro...

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.

In this paper, a set of conditions for defining output functions is derived such that a system can become passive by static state feedback. A key feature of the proposed set of conditions is that several output functions that satisfy these conditions can be defined without any transformation into a normal form or any need of linearisation of the system. In essence, this paper introduces a new connection between the Lyapunov stability concept and minimum phase property which is indeed a necessary condition of feedback passivity.

This paper studies adaptive finite-time control problem of a class of nonlinear systems with dynamic and parametric uncertainties. The power of systems in this paper is any positive odd rational number but not necessary equal to or greater than one. To solve this problem, finite-time input-to-state stability (FTISS) is used to characterise the unmeasured dynamic uncertainty. By skillfully combining Lyapunov function, sign function, adding a power integrator, adaptive control and FTISS methods, an adaptive state feedback controller is designed to drive the states to the origin in finite time while maintaining all the closed-loop signals bounded.

This paper investigates the adaptive finite-time modified function projective lag synchronisation with definite attenuation rate for a class of chaotic systems with unknown parameters. Firstly, a novel integral type terminal sliding surface is proposed to ensure the finite-time stability of the sliding motion. Subsequently, an adaptive finite-time control law is designed to guarantee the occurrence of the sliding motion in a limited time and the accurate estimation of the unknown parameters. Moreover, the exponential attenuation rate designed in this paper can further cut down the synchronisation time. Numerical simulations are presented to verify the feasibility and effectiveness of the proposed method.

This paper proposes a new adaptive gain robust model-following/tracking controller for a class of uncertain linear systems. The proposed adaptive gain robust controller is composed of state feedback laws with fixed gains, feedfoward inputs for the reference model and nonlinear compensation inputs with adjustable time-varying parameters. Moreover, the proposed control strategy can achieve good transient performance and avoid the excessive control input by means of the design parameter. In this paper, linear matrix inequality-based sufficient conditions for the existence of the proposed adaptive gain controller are given, and two design strategies are presented. Finally, simple illustrative examples are included to show the effectiveness of the proposed adaptive gain robust controller.

This paper is concerned with the distributed optimisation problem over a multi-agent network, where the objective function is described by a sum of all the local objectives of agents. The target of agents is to collectively reach an optimal solution while minimising the global objective function. Under the assumption that the information exchange among agents is depicted by a sequence of time-varying undirected graphs, a distributed optimisation algorithm with uncoordinated time-varying step-sizes is presented, which signifies that the step-sizes of agents are not always uniform per iteration. In light of some reasonable assumptions, this paper fully conducts an explicit analysis for the convergence rate of the optimisation method. A striking feature is that the algorithm has a geometric convergence rate even if the step-sizes are time-varying and uncoordinated. Simulation results on two numerical experiments in power systems show effectiveness and performance of the proposed algorithm.

Leaderless consensus for the fractional-order nonlinear multi-agent systems is investigated in this paper. At the first part, a control protocol is proposed to achieve leaderless consensus for the nonlinear single-integrator multi-agent systems. At the second part, based on sliding mode estimator, a control protocol is given to solve leaderless consensus for the the nonlinear single-integrator multi-agent systems. It shows that the control protocol can improve the systems’ convergence speed. At the third part, a control protocol is designed to accomplish leaderless consensus for the nonlinear double-integrator multi-agent systems. To judge the systems’ stability in this paper, two classic continuous Lyapunov candidate functions are chosen. Finally, several worked out examples under directed interaction topology are given to prove above results.

This paper investigates distributed connectivity-preserving consensus problems of networked multi-agent systems with limited communication ranges. Compared with existing literature, a main contribution of this paper is to present a new nonlinear transformation approach of consensus errors for preserving the initial interaction patterns of multi-agent systems. Both the consensus and the connectivity preservation can be achieved by using one transformed error function. Based on the proposed nonlinear error transformation, we derive distributed connectivity-preserving consensus algorithms for single-integrator dynamics, double-integrator dynamics and second-order nonlinear systems. The asymptotic stability of consensus errors and the connectivity preservation among agents are established through Lyapunov stability analysis.

This paper concentrates on the dynamical behaviours of memristor-based complex-valued delayed neural networks. By constructing the appropriate Lyapunov functional and utilising some inequality techniques, sufficient conditions are proposed to guarantee the existence and global exponential stability of the periodic solution of the considered system. The proposed results not only generalise some previously related literatures, but also are easy to be checked with the parameters of system itself. In addition, the theoretical results of this paper may be helpful in qualitative analysis for complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of the proposed results.

In this paper, the fault detection problem is investigated for a class of discrete-time piecewise linear systems with external disturbances and infinite distributed time-delays. As a modelling framework, piecewise linear system often arise when piecewise linear components are encountered, such as dead-zone, saturation, relays and hysteresis. The time-delays are assumed to be infinitely distributed in the discrete-time domain. The aim of this paper is to detect the possible faults and to estimate the system state. For this purpose, firstly, stability analysis is given based on a piecewise smooth Lyapunov function. Afterward, an appropriate approach of fault detection and filter design problem is provided to achieve a satisfactory balance between the disturbances attenuation level γ and the sensitivity to the fault for piecewise linear systems. As a consequence, a sufficient condition is obtained in terms of the linear matrix inequalities such that, for all admissible infinite distributed time-delay...

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

The proposition of U-model concept in terms of ‘providing concise and applicable solutions for complex problems’ and a corresponding basic U-control design algorithm was originated in the first author's PhD thesis. The term of U-model appeared not rigorously defined for the first time in the first author's other journal paper, which established a framework for using linear polynomial control system design approaches to design nonlinear polynomial control systems in brief, linear polynomial approaches → nonlinear polynomial plants. This paper represents the next milestone work – using linear state-space approaches to design nonlinear polynomial control systems in brief, linear state-space approaches → nonlinear polynomial plants. The overall aim of the study is to establish a framework, defined as the U-block model, which provides a generic prototype for using linear state-space-based approaches to design the control systems with smooth nonlinear plants/processes described by polynomial models. For analysing the feasibility and effectiveness, sliding mode control design approach is selected as an exemplary case study. Numerical simulation studies provide a user-friendly step-by-step procedure for the readers/users with interest in their ad hoc applications. In formality, this is the first paper to present the U-model-oriented control system design in a formal way and to study the associated properties and theorems. The previous publications, in the main, have been algorithm-based studies and simulation demonstrations. In some sense, this paper can be treated as a landmark for the U-model-based research from intuitive/heuristic stage to rigour/formal/comprehensive studies.

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

This paper presents and analyses the continuous review inventory model with order quantity, safety factor, back-order price discount, ordering cost and lead time as decision variables. Our work is based on the paper of Huang 2010. We extend the model to incorporate the situation when the amount received is uncertain. The lead time demand is assumed follows a normal distribution. A solution procedure is developed to find the optimal solution. A numerical example is given to illustrate the model. A sensitivity analysis is also included to describe the effects of changes in the model parameters on the expected annual cost.

In this paper we present two solution procedures to obtain the solution for a family of economic production quantity inventory models for an integrated vendor–buyer system considering that production system generates defective products, in which the number of shipments must be a discrete value and that the lot size can take continuous case 1 or integer case 2 values. These solution procedures provide a more realistic approach for solving a family of inventory models. Previous works only derive the optimal lot size considering the number of shipments as a given value or derive both the lot size and the number of shipments considering both decision variables as continuous. The number of shipments takes integer values in real life and thus it must be considered as a discrete variable. This paper revisits and solves previous inventory models considering the variables lot size and number of shipments according to their real-life nature.

This study models a joint location, inventory and preservation decision-making problem for non-instantaneous deteriorating items under delay in payments. An outside supplier provides a credit period to the wholesaler which has a distribution system with distribution centres DCs. The non-instantaneous deteriorating means no deterioration occurs in the earlier stage, which is very useful for items such as fresh food and fruits. This paper also considers that the deteriorating rate will decrease and the reservation cost will increase as the preservation effort increases. Therefore, how much preservation effort should be made is a crucial decision. The objective of this paper is to determine the optimal locations and number of DCs, the optimal replenishment cycle time at DCs, and the optimal preservation effort simultaneously such that the total network profit is maximised. The problem is formulated as piecewise nonlinear functions and has three different cases. Algorithms based on piecewise nonlinear optimisation are provided to solve the joint location and inventory problem for all cases. Computational analysis illustrates the solution procedures and the impacts of the related parameters on decisions and profits. The results of this study can serve as references for business managers or administrators.

In an indeterminacy economic environment, experts’ knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

International audience; Embedded devices using highly integrated chips must cope with conflicting constraints, while executing computationally demanding applications under limited energy storage. Automatic control and feedback loops appear to be an effective solution to simultaneously accommodate for performance uncertainties due to the tiny scale gates variability, varying and poorly predictable computing demands and limited energy storage constraints. This paper presents the example of an embedded video decoder controlled by several feedback loops to carry out the trade-off between decoding quality and energy consumption, exploiting the frequency and voltage scaling capabilities of the chip. The inner loop controls the Dynamic Voltage and Frequency Scaling (DVFS) through a fast predictive control strategy. The outer loop computes the scheduling set-points needed by the inner loop to process frames decoding. The feedback loops have been implemented on a stock PC and experimental results are provided. The upcoming generations of embedded devices are integrating more and more multimedia and telecommunication applications, such as PDAs, mobile phones and tablets, thus requiring increasing on-board computing power. On the other hand, they become even more miniaturised (which means with limited energetic resources) while needing increased autonomy. These constraints are clearly conflicting, therefore technological evolutions are needed to improve their power consumption, computational efficiency and fabrication yield. In this paper, we propose the use of feedback control loops as a possible solution. The approach is applied to an embedded video decoding device whose requirements can be categorised in three main points : a) Energy saving requirements : In current CMOS integrated chips, Dynamic Voltage and Frequency Scaling (DVFS) can be used to efficiently manage the energy consumption of a device. It results in simultaneously managing the frequency and the voltage (note that scaling down the voltage increases signals delays along the paths through electronic gates, thus needing to decrease its clock frequency as well, and inversely (Chandrakasan and Brodersen (1995), Flautner et al. (2004), Varma et al. (2003))). In many cases, the only performance requirement is that the tasks meet their deadline. Such cases create opportunities to run the processor at a lower computing level and achieve the same perceived performance while consuming less energy.A closed-loop DVFS approach is hence a good solution for energy saving. Classically, each task is considered independently, running at a constant voltage whose value is set to meet the deadline, and even selecting a small number of voltage levels leads to a drastic energy reduction (Ishihara and * Corresponding author.

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.

This paper's aim is to present a novel design technique for distributed estimation in networked systems. The problem assumes a network of interconnected agents each one having partial access to measurements from a linear plant and broadcasting their estimations to their neighbours. The objective is to reach a reliable estimation of the plant state from every agent location. The observer's structure implemented in each agent is based on local Luenberger-like observers in combination with consensus strategies. The paper focuses on the following network related issues: delays, packet dropouts and communication policy (time and event-driven). The design problem is solved via linear matrix inequalities and stability proofs are provided. The technique is of application for sensor networks and large scale systems where centralized estimation schemes are not advisable and energy-aware implementations are of interest. Simulation examples are provided to show the performance of the proposed methodologies.

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.

N-version programming is a useful approach to improve the quality of software, especially for safety-critical systems. Positive performance in enhancing software availability is an expected result. In this paper, a software availability model for the study of the impact of N-version programming technique is proposed and investigated. The characteristics of the N-version software system and its operation and failure process are analysed. Based on this analysis, the time-dependent behaviour of the software system, which alternates between online and offline states, is described using a Markov chain. This model derives quantitative measures of software availability. Numerical examples and comparisons are also presented in this paper to directly illustrate N-version programming's positive impact on software availability measures. N-version programming generally provides a positive impact on the system. However, it does not always guarantee a higher availability performance. General recommendations are provide...

This paper presents an unobservable single-server queueing system with three types of uncertainty, where the service rate, or waiting cost or service quality is random variable that may obtain n(n > 2) values. The information about the realised values of parameters is only known to the server. We are concerned about the server’s behaviour: revealing or concealing the information to customers. The n-value assumption and the server’s behaviour enable us to consider various pricing strategies. In this paper, we analyse the effect of information and uncertainty on profits and make comparisons between the profits under different pricing strategies. Moreover, as for parameter variability reflected by the number of each parameter’s possible choices n, we observe the effect of variable n on all types of profits and find that revealing the parameter information can much more benefit the server with the increase of n.

This paper describes the application of basis concepts in semiotics and nonsmooth systems to robot control. The resulting framework yields a vocabulary of movements tailored to the control of semi-autonomous robots. Semiotics provides a set of classes of objects to model the interactions among humans. The paper proposes the use of these semiotic objects to model human–robot interactions, accounting for features such as semantics and ambiguity. Mathematical models for the objects are presented including objects to generate paths for the robot to follow and operators to act on such objects. Experiments with single and multiple robots illustrating the main aspects of the proposed framework are presented.

To evaluate the software-related measure such as the first passage time and software availability, we consider in this paper an imperfect software debugging model incorporating two types of fault, one being easily detected and the other difficult to detect. When a software failure occurs, the fault that causes the failure either is removed from the fault contents or still remained in the system even after the debugging is completed. In this sense, the debugging model is referred to as an imperfect model. In this paper, we first define the transition probabilities of the debugging process and use it to derive several parameters of interest measuring the software performance, including the expected numbers of perfect debuggings. The availability function of a software system, which is defined as the probability that the software is in a working state at a given time, is also derived. We assume that the length of time necessary for debugging the failure is random in all evaluations. Numerical examples are gi...

This paper extends research in the area of biologically inspired, discrete dynamical basis networks (DDBNs). While similar to locally recurrent globally feedforward (LRGF) networks (Tsoi and Back 1994), DDBNs operate at a lower level of abstraction and were inspired by research in the areas of Control Systems, Artificial Intelligence, and Neurobiology. As described previously, DDBNs can approximate data sequences and consist of networks of simple, bounded mathematical operators (Jones and Olmsted 2003). This paper examines the characteristics of genetically designed DDBNs and compares them with tree-based genetic programs (TBGPs), biological neural networks, and backpropagation neural networks (NNs). Experimental evidence indicates that DDBNs are capable of computing simple logic functions in addition to approximating data sequences.

Non-cooperative decision-making problems in a decentralized supply chain can be characterized and studied using a stochastic game model. In an earlier paper, the authors developed a methodology that uses machine learning for finding (near) optimal policies for non-zero sum stochastic games, and applied their methodology on an N-retailer and W-warehouse inventory-planning problem. The focus of this paper is on making the methodology more amenable to practical applications by making it completely simulation-based. It is also demonstrated, through numerical example problems, how this methodology can be used to find (near) equilibrium policies, and evaluate short-term rewards of stochastic games. Short-term rewards of stochastic games could be, in many instances, more critical than equilibrium rewards. To our knowledge, no methodology exists in the open literature that can capture the short-term behaviour of non-zero sum stochastic games as examined in this paper.

As industrial systems become increasingly more complex, the maintenance and operating information increases both in volume and complexity. With the current pressures on manufacturing, the management of information resources has become a critical issue. In particular, ensuring that personnel can access current information quickly and effectively when undertaking a specific task. This paper discusses some of the issues involved in, and the benefits of using, open hypermedia to manage and deliver a diverse range of information. While the paper concentrates on the problems specifically associated with manufacturing organizations, the problems are generic across other business sectors, such as healthcare, defence and finance. The open hypermedia approach to information management and delivery allows a multimedia resource base to be used for a range of applications and it permits a user to have controlled access to the required information in an easily accessible and structured manner. Recent advancement in hyp...

This paper presents a new recursive hybrid algorithm for training a radial basis function (RBF) network. The algorithm consists of a proposed clustering algorithm to position the RBF centres and the Givens least-squares algorithm to estimate the weights. This paper begins with a discussion about the problems of clustering in positioning RBF centres. Then a new clustering algorithm called adaptive fuzzy c-means clustering algorithm is proposed to reduce the problems. The capability of the proposed algorithm was tested to model three data sets: one simulated and two real data sets. It was found that the algorithm provided good performance. The performance of the algorithm was then compared with adaptive k-means, non-adaptive k-means and non-adaptive fuzzy cmeans clustering algorithms. Overall performance of the RBF network that used the proposed clustering algorithm was found to be much better than those that used other clustering algorithms. Simulation results also revealed that the algorithm was not sensi...

This paper addresses the problem of the robust decentralized control for nonlinear interconnected systems with uncertainties. The similar structures of nonlinear interconnected systems are first defined. Then, controllers are designed for these systems to solve the problems of robust decentralized output tracking and robust decentralized stabilization via static output feedback. The results obtained in this paper show that the analysis and design of systems can be simplified by using the similar structures of systems. Simulation examples are given to show the effectiveness of the method.

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

This paper introduces an approach to the modelling phase intervening in the design or control process of production systems. The suggested modelling is based on the use of the state formalism to represent the evolution of the production flow in the different sections of the studied or considered system. It is suggested that one proceeds to the systematic writing of the state representation based on the typology of the physical system with the assistance of semantic groupings attached to the types and the localization of the entities. This approach has a double interest. It enables, on the one hand, one to resort directly to simulations for the evaluation of performances and, on the other hand, to carry out the synthesis of a control system using conventional automation tools. An application, implementing the main elementary models, concludes the paper.

Taxis, the tendency of an organism to approach or retreat from an external stimulus, is a common property of mobile living organisms. Taxis may be observed, e.g. in single-celled organisms, paramecia and Escherichia coli. Control in these organisms is implemented by chemical means. Shortly after multi-celled organisms appeared, their cells began to specialize. Early mobile multi-celled organisms also demonstrate taxis. For example, the pillbug seeks light. In medusa (jellyfish) of Phylum cnidaria the control task is accomplished by neuronal methods. The author is aware of no reports of jellyfish with associative memories. Thus, there exist multi-celled organisms that can control the motion of their bodies, yet possess no memory capacities. We speculate that the first task of neuronal circuitry was that of control rather than that of memory. In this paper we discuss the properties of neurons that make them natural controllers. It is the thesis of this paper that the ability to control is an emergent proper...

This paper addresses the problem of specification and formal verification of complex applications in advanced robotics systems. In the first part, the need for such studies is presented, and the state of the art in the field is given, ranging from computer science to robotics. Then, the key features used in the paper are presented. They are called the robot task and the robot procedure respectively and allow us to specify in a structured way all the elements of robot controllers from the continuous and discrete time specification to implementation aspects. They are both integrated in the ORCCAD design environment. In the following, verification issues are described in depth, from the logical and temporal point of view. They are illustrated by a real example of automatic vehicle driving, in which various properties are proved and abstract views are built. The conclusion gives an evaluation of the obtained results, expresses some requirements and draws guidelines for the future. The interest of hybrid syste...

In this paper, the asynchronous H∞ filtering problem of discrete-time networked switched systems under effective DoS attacks is studied. DoS attacks launched by the opponent are not always successf...

In this paper, an adaptive generalised function projective synchronisation scheme of fractional-order chaotic systems with different dimensions and orders and fully unknown parameters is presented....

Client-server computing provides many corporations with the ability to build scalable, adaptive and cost-effective information systems. However, to design and implement an effective client-server system is not an easy job. The complexity due to the inherent heterogeneity in client-server platforms makes the performance evaluation an important issue. The main purpose of this paper is to provide an analytical model to evaluate the performance of a client-server system before it is implemented. It is shown in this paper that the Petri net model is a powerful technique used to study the client-server system performance. Besides using a numerical example to illustrate the solution procedure of the Petri net model, a case study is also given to show the applicability of the developed analytical model. This research concludes that, with the assistance of some software packages, the Petri nets are very useful tools for the information system manager to analyse a client-server system and to decide the appropriate ...

This paper deals with an object-oriented approach to analysis, design, and implementation of constraint logic programming (CLP) for a timetabling system (TTS). Currently, it is difficult to maintain and port such systems to different environments. This is because by nature, timetabling problems are combinatorial, dynamics, and optimization-based. Using a conventional approach, changes to organization's policies and activities require updating of the model. Subsequently, computer programs need to be updated accordingly. These tasks have to be performed by experts who are scarcely available. In this paper, we show that maintainability and extendibility of object-oriented technologies enhance model formulation and maintenance by inheritance and polymorphism properties of classes and objects. The object-oriented approach also enhances constraint propagation and search by object interactions and communications. These advantages can be applied to other similar optimization-based systems such as planning and sch...

A Rule-Based Information Distribution System (RBIDS) is a system that uses distributed rules to control message passing (communications) between distributed nodes within a real-time, concurrent distributed environment. The problem addressed in this paper is that there do not exist adaptation (debugging, error removal) algorithms that can be used to maintain existing information-distribution rules (IDRs) to achieve effective and efficient message-communication responses in a distributed system of fact and rule bases. A partial solution to this problem is presented in the paper. This paper presents an algorithm that is used to overcome dynamic erors that have occurred in a RBIDS environment.

The existing techniques for data compression can be divided into three main groups, namely direct, transformation and parameter extraction methods. The present paper deals with the direct data compression (DDC) methods as applied to ECG data. The performance has been evaluated on the basis of compression ratio, percent-root-mean-square difference and fidelity of the reconstructed signal. Further, in order to know the extent to which the diagnostic information is preserved during compression, peak and boundary measurements have been made both on the reconstructed and original ECG signal and compared. The objective of the present paper is to report the effect of sampling frequency on the aforementioned parameters as studied by the authors. The experimental results show that with the increase in sampling frequency the compression ratio increases and the percent-root-mean-square difference generally decreases. Further, the reconstructed signal is of higher quality having larger bandwidth and higher resolution...

The paper addresses the problem of modelling an information flow in manufacturing agents (subsystems) consisting of people, machines, robots, etc. It matches very frequent real-life situations in which the following should be answered. How to structure an exchange of information between the elements of an agent? What is better, complete information but heavily delayed, or incomplete information less delayed? The formal mathematical model can be used to generate some examples of knowledge that is useful in answering the above type of questions. Quantitative models of an information flow evaluation, however, are often too complex to serve as the tools useful in the knowledge retrieval process. This realization, also observed in other areas of applied mathematical modelling, has led to increasing interest and research in non-quantitative modelling. In the paper, the application of signed directed graphs in the process of knowledge acquisition for autonomous agents is investigated and illustrated with the cas...

In this paper the random balanced solution to forward-lag-type and mixed-lag-type dynamic input-output models is given. The exact value of the essential time-lag parameter for both models cannot usually be found, especially in the case of economies in transition. The reason for this is the lack of pertinent statistical data. The problems with statistical data are caused by changes of the structure of the economy accompanied by a high rate of inflation. Some data are still computed using old methods typical for the centrally planned economy and the collection of the other data follows by means of market-oriented methods. For this reason different data are not compatible, especially those that concern investment outlays. Usually, experts can establish the most probable or expected value of time-lag. Thus, the growth factor and, consequently, the respective balanced growth paths of both models are random. In this paper the general formulas for stochastic characteristics of both kinds of models are derived

This paper addresses the problem of developing the domain knowledge base for autonomous manufacturing agents (subsystems). In an organizational and behavioural context, autonomous agents consist of elements (people, machines, robots, etc.) tied by the flow of information between an agent and its external environment as well as within an agent. The paper focuses on non-quantitative support that can be used for reasoning and retrieval of knowledge describing such a flow of information in various decision situations. The formal quantitative model can be used to generate some examples of the above domain knowledge. Quantitative models of an information flow evaluation, however, are often too complex to serve as the tools useful in the knowledge retrieval process. In the paper, the application of such artificial intelligence tools as decision trees and neural networks is investigated and illustrated with examples

In this paper, an event-triggered P-type iterative learning control (ILC) scheme is developed, where the control input update only occurs in the event-triggered iterations. The event-triggered iter...

This paper first investigates robust tracking problem of a high-order iterative learning control (HOILC) algorithm for two classes of two-dimensional linear discrete time-varying Fornasini–Marchesi...