Showing papers in "International Journal of Systems Science in 2008"
TL;DR: The equivalent injection signal in problems relating to fault detection and condition monitoring is demonstrated and the literature in the area is presented and qualified in the context of continuing developments in the broad areas of the theory and application of sliding mode observers.
Abstract: Sliding mode observers have unique properties, in that the ability to generate a sliding motion on the error between the measured plant output and the output of the observer ensures that a sliding mode observer produces a set of state estimates that are precisely commensurate with the actual output of the plant. It is also the case that analysis of the average value of the applied observer injection signal, the so-called equivalent injection signal, contains useful information about the mismatch between the model used to define the observer and the actual plant. These unique properties, coupled with the fact that the discontinuous injection signals which were perceived as problematic for many control applications have no disadvantages for software-based observer frameworks, have generated a ground swell of interest in sliding mode observer methods in recent years. This article presents an overview of both linear and non-linear sliding mode observer paradigms. The use of the equivalent injection signal in problems relating to fault detection and condition monitoring is demonstrated. A number of application specific results are also described. The literature in the area is presented and qualified in the context of continuing developments in the broad areas of the theory and application of sliding mode observers.
486 citations
TL;DR: The purpose of this article is to survey the recent results developed to analyse the asymptotic stability of time-delay systems and give special emphases to the issues of conservatism of the results and computational complexity.
Abstract: Recent years have witnessed a resurgence of research interests in analysing the stability of time-delay systems. Many results have been reported using a variety of approaches and techniques. However, much of the focus has been laid on the use of the Lyapunov-Krasovskii theory to derive sufficient stability conditions in the form of linear matrix inequalities. The purpose of this article is to survey the recent results developed to analyse the asymptotic stability of time-delay systems. Both delay-independent and delay-dependent results are reported in the article. Special emphases are given to the issues of conservatism of the results and computational complexity. Connections of certain delay-dependent stability results are also discussed.
385 citations
TL;DR: A systematic overview of basic research on model selection approaches for linear-in-the-parameter models, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design is presented.
Abstract: The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.
223 citations
TL;DR: An actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered and a simulation study of the HIRM aircraft system is presented to show the effectiveness of the scheme.
Abstract: In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The reconstructed signal can approximate the system fault signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.
139 citations
TL;DR: The purpose of this study is to investigate an integrated production inventory deteriorating model considering the pricing policy, the imperfect production, the inspection planning, the warranty-period and the stock-level-dependant demand with the Weibull deterioration, partial backorder and inflation.
Abstract: In marketing, enterprises try all motivated selling strategies to stimulate customers to buy a product. One of these selling strategies is a warranty policy that provides a return promise of free replacement. The buyer may place more orders because of the display of the product. An increasing demand resulting from these motivated factors influences the replenishment planning. In operational process, quality level resulting in relevant activities may cause changes of operational planning. The purpose of this study is to investigate an integrated production inventory deteriorating model considering the pricing policy, the imperfect production, the inspection planning, the warranty-period and the stock-level-dependant demand with the Weibull deterioration, partial backorder and inflation. We incorporate a single-retailer single-manufacturer cooperation from the perspectives of both the manufacturer and the retailer. The classical optimisation technique and the heuristic method are used to derive the optimum solutions. A numerical example and sensitivity analysis are presented.
68 citations
TL;DR: A methodological and structured framework, which makes use of both qualitative and quantitative techniques for risk and reliability analysis of the system, and a risk ranking approach based on fuzzy and Grey relational analysis is proposed to prioritize failure causes.
Abstract: The main objective of the article is to permit the reliability analyst's/engineers/managers/practitioners to analyze the failure behavior of a system in a more consistent and logical manner. To this effect, the authors propose a methodological and structured framework, which makes use of both qualitative and quantitative techniques for risk and reliability analysis of the system. The framework has been applied to model and analyze a complex industrial system from a paper mill. In the quantitative framework, after developing the Petrinet model of the system, the fuzzy synthesis of failure and repair data (using fuzzy arithmetic operations) has been done. Various system parameters of managerial importance such as repair time, failure rate, mean time between failures, availability, and expected number of failures are computed to quantify the behavior in terms of fuzzy, crisp and defuzzified values. Further, to improve upon the reliability and maintainability characteristics of the system, in depth qualitative analysis of systems is carried out using failure mode and effect analysis (FMEA) by listing out all possible failure modes, their causes and effect on system performance. To address the limitations of traditional FMEA method based on risky priority number score, a risk ranking approach based on fuzzy and Grey relational analysis is proposed to prioritize failure causes.
66 citations
TL;DR: This article considers networked non-linear control for an aluminum plate thermal process by using operator-based robust right coprime factorisation approach and Bezout identity to guarantee robust stable networked control system.
Abstract: This article considers networked non-linear control for an aluminum plate thermal process. This is done by using operator-based robust right coprime factorisation approach and Bezout identity to guarantee robust stable networked control system, as well as an operator-based tracking controller to ensure the controlled process output tracking the desired reference input. An experimental result is given for the case of temperature control of the aluminum plate thermal process.
55 citations
TL;DR: This article deals with the fault detection problem for a class of discrete-time networked systems with multiple state delays and unknown input and proposes a design of a fault detection filter such that the error between residual and weighted fault is made as small as possible.
Abstract: This article deals with the fault detection problem for a class of discrete-time networked systems with multiple state delays and unknown input. Two kinds of incomplete measurements, namely measurements with random communication delays and measurements with stochastic packet losses, are simultaneously considered. By properly augmenting the states of the original system and the fault detection filter, the fault detection filter design problem can be formulated as an H∞ filtering problem. Attention is focused on the design of a fault detection filter such that, for all unknown input and incomplete measurements, the error between residual and weighted fault is made as small as possible. A sufficient condition for the existence of the desired fault detection filter is established in terms of a linear matrix inequality. A numerical example is provided to illustrate the effectiveness and applicability of the proposed techniques.
54 citations
TL;DR: This article addresses the stabilisation of non-linear systems represented by a discrete-time Takagi–Sugeno model based on an extended non-quadratic Lyapunov function and a non-parallel distributed compensation (non-PDC) law, with results more relaxed than some existing ones.
Abstract: This article addresses the stabilisation of non-linear systems represented by a discrete-time Takagi-Sugeno model. Based on an extended non-quadratic Lyapunov function and a non-parallel distributed compensation (non-PDC) law, some additional slack matrices are introduced. Compared with the existing methods, which collect the interactions among the subsystems into a sequence of collection matrices, the new method re-collects the newly introduced slack matrices into another collection matrix. In this way, a stability result more relaxed than some existing ones are obtained. Further, the convexity of the fuzzy blending rules is utilised, with a further improved result obtained. The corresponding robust stability results are also proposed. The effectiveness of the new results is validated by two simulation examples.
54 citations
TL;DR: It is envisaged that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.
Abstract: The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naive Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.
51 citations
TL;DR: This article considers the design and stability of networked control systems with random delay in signal transmission channels and proposes a networked predictive control strategy for discrete networked systems.
Abstract: This article considers the design and stability of networked control systems with random delay in signal transmission channels. To deal with the random delay, a networked predictive control strategy is proposed for discrete networked systems. The key parts of the control strategy are the control prediction generator that provides a set of future control predictions and the network delay compensator that compensates for the network transmission delay. The analytical stability criteria of the closed-loop networked predictive control systems are derived for both fixed and random network delays. The proposed networked predictive control method is illustrated using simulations and practical Intranet-/Internet-based control experiments.
TL;DR: The free-weighting-matrix approach is developed to study the H∞ control of linear discrete-time systems with an interval-like time-varying delay and a memoryless H ∞ state-feedback controller is designed based on a performance analysis.
Abstract: The free-weighting-matrix approach is developed to study the H∞ control of linear discrete-time systems with an interval-like time-varying delay. First, a delay-and range-dependent criterion for a given H∞ performance is derived. Second, a memoryless H∞ state-feedback controller is designed based on a performance analysis. Finally, two numerical examples demonstrate the effectiveness of the proposed method and show that both the upper bound and range of an interval-like time-varying delay affect the stability and/or H∞ performance of a system.
TL;DR: This paper considers the stabilisation problem for uncertain discrete-time switched systems with time-varying delay in the state by using switched Lyapunov function, which is dependent on the minimum and maximum delay bounds.
Abstract: This paper considers the stabilisation problem for uncertain discrete-time switched systems with time-varying delay in the state. The uncertainty is assumed to be of structured linear fractional form, which includes the norm-bounded uncertainty as a special case. A stability condition is first proposed by using switched Lyapunov function, which is dependent on the minimum and maximum delay bounds. Based on the result, a switched state feedback controller is designed. Numerical examples are given to illustrate the effectiveness of the result.
TL;DR: A functional approximation (FA) based adaptive sliding controller with fuzzy compensation is proposed for an active suspension system that can suppress the oscillation amplitude of the sprung mass effectively and guarantee the control system stability.
Abstract: Active suspension systems are designed to provide better ride comfort and handling capability in the automotive industry. Since the active suspension system has nonlinear and time-varying characteristics, it is difficult to establish an accurate dynamic model for designing a model-based controller. Here, a functional approximation (FA) based adaptive sliding controller with fuzzy compensation is proposed for an active suspension system. The FA technique is employed to represent the unknown functions, which releases the model-based requirement of the sliding mode control. In addition, a fuzzy control scheme with online learning ability is employed to compensate for the modeling error of the FA with finite number of terms for reducing the implementation difficulty. To guarantee the control system stability, the update laws of the coefficients in the approximation function and the fuzzy tuning parameters are derived from the Lyapunov theorem. The proposed controller is employed on a quarter-car active suspension system. The simulation results and experimental results show that the proposed controller can suppress the oscillation amplitude of the sprung mass effectively. To evaluate the performance improvement of inducing a fuzzy compensator in this FA adaptive controller, the dynamic responses of the proposed hybrid controller are compared with those of FA-based adaptive sliding controller only.
TL;DR: A new delay-dependent adaptive law is proposed to design the adaptive reconfigurable controller, which is excited to offset the effect of faults and disturbance automatically.
Abstract: This article deals with the problem of adaptive fault-tolerant control against unknown actuator faults for a class of nonlinear time delay systems with disturbance. The actuator faults are types of loss of effectiveness. The aim is to find an adaptive fault tolerant controller, such that the system is not only stabilized, but also the state vectors of normal and fault cases with disturbance track that of the normal case without disturbance, which has the designed performance. A new delay-dependent adaptive law is proposed to design the adaptive reconfigurable controller, which is excited to offset the effect of faults and disturbance automatically. Numerical and simulation results are provided to demonstrate the effectiveness of the proposed controller.
TL;DR: The main aim of this article is to derive general conditions for a few types of controllability at once for an arbitrary order abstract differential equation and arbitrary eigenvalues multiplicities, instead of conditions for fixed order equation and single eigen values.
Abstract: The main aim of this article is to derive general conditions for a few types of controllability at once for an arbitrary order abstract differential equation and arbitrary eigenvalues multiplicities, instead of conditions for fixed order equation and single eigenvalues. Another innovation of this article is taking into account delays caused by electronic control microcontrollers. This was possible thanks to analysis of the n-th order linear system in the Frobenius form, generating Jordan transition matrix of the confluent Vandermonde form. Using the explicit analytical form of the inverse confluent Vandermonde matrix enabled us to receive general conditions of different types of controllability for the infinite dimensional systems. We derived this analytical form of the inverse confluent Vandermonde matrix using new results from the linear algebra, presented in the paper by S. Hou and W. Pang, “Inversion of confluent Vandermonde matrices”, Int. J. Comput. Math. Appl., 43, pp. 1539-1547, 2002.
TL;DR: The objective of this study is to develop an optimal replenishment inventory strategy to consider both ameliorating and deteriorating effects taking account of time value of money and finite planning horizon.
Abstract: The objective of this study is to develop an optimal replenishment inventory strategy to consider both ameliorating and deteriorating effects taking account of time value of money and finite planning horizon. The amelioration rate and the deterioration rate are assumed to follow a Weibull distribution. The inventory system is particularly useful for young livestock whose utility increase over time. The discounted cash flow and optimisation technique are used to derive an optimal solution. A numerical example and sensitivity analysis are given to illustrate the theory of the inventory system.
TL;DR: Two novel swing-up control strategies for a reaction wheel pendulum have been proposed, one based on interconnection and damping assignment-passivity based control (IDA-PBC), which provides fast responses as compared to existing energy based schemes.
Abstract: Control of a reaction wheel pendulum, a prototype of an under-actuated system, is easily done using switching control strategies, which combines swing-up control and balancing control schemes. In this article, two novel swing-up control strategies for a reaction wheel pendulum have been proposed. The first swing-up control strategy treats the oscillations of the pendulum as perturbations from the bottom equilibrium point. The second swing-up control is based on interconnection and damping assignment-passivity based control (IDA-PBC). IDA-PBC preserves Euler Lagrangian structure of the system and gives more physical insight about any mechanical system. Any balancing controller can be coupled with the proposed swing-up control strategies to stabilise the pendulum at the top unstable equilibrium position. The control task of balancing the pendulum in top upright position is completed by switching from swing-up scheme to the balancing scheme at the point where the pendulum is very near to the top equilibrium point. Proposed swing-up control strategies have been implemented in real time in switching mode. The two proposed swing-up control schemes provide fast responses as compared to existing energy based schemes.
TL;DR: The aim of this article is to derive the expected cost rate per unit time by introducing relative costs as a criterion of optimality, and then the optimal replacement period which minimizes that cost will be determined.
Abstract: In this article, a periodical replacement model for a two-unit system which is both subjected to failure rate interaction and external shocks will be presented. Without external shocks, each unit 1, whenever it fails, will act as an interior shock to affect the failure rate of unit 2 and increase the failure rate of unit 2 to a certain degree, while each unit 2 failure causes unit 1 into instantaneous failure. Besides failure rate interaction between units, the system is also subjected to external shocks which can be divided into two types. Type A shock causes unit 1 into failure and then converts the damage of such a failure to unit 2, while type B shock makes the system total breakdown. All unit 1 failures are corrected by minimal repairs. The aim of this article is to derive the expected cost rate per unit time by introducing relative costs as a criterion of optimality, and then the optimal replacement period which minimizes that cost will be determined. A numerical example is given to illustrate the method.
TL;DR: A hybrid genetic algorithm is developed for this NP-hard decision problem and it is extended to systems with resource restrictions to resolve the supplier selection problem when considering the quantity discounts.
Abstract: In this article, we deal with the problem of determining the economic operating policy when a number of items are to be procured from a number of suppliers offering different quantity discounts schedules. In such inventory problems, a fixed cost is incurred with each replenishment order, independent of the suppliers as well as the items involved in the order. Further, the item involves a minor fixed cost. In such a system, it includes the supplier selection problem when considering the quantity discounts as well as the general joint replenishment problem. We develop a hybrid genetic algorithm for this NP-hard decision problem and extend it to systems with resource restrictions.
TL;DR: An iterative subspace system identification algorithm for MIMO linear parameter-varying systems with innovation-type noise models driven by general inputs and a measurable white noise time-Varying parameter vector based on a convergent sequence of linear deterministic–stochastic state-space approximations is introduced.
Abstract: In this article, we introduce an iterative subspace system identification algorithm for MIMO linear parameter-varying systems with innovation-type noise models driven by general inputs and a measurable white noise time-varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard-based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. Their greatest strength lies on the dimensions of the data matrices that are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.
TL;DR: A mixed-integer linear programming model is suggested which is embedded in a dynamic procedure simulating a rolling horizon planning process and takes into account flexible demands and to evaluate different planning strategies to face with these flexible demands.
Abstract: In order to support decision making, this article investigates the planning process of a production unit within a supply chain The aim is to satisfy the customer demand while respecting the internal constraints of the production unit and those of its supply chain partners In that purpose, we suggest a mixed-integer linear programming model which is embedded in a dynamic procedure simulating a rolling horizon planning process A special attention is given to the temporal features of the production unit and of its suppliers (cycle times, anticipation delays) as well as those of the planning process itself (planning horizon, frozen horizons, planning periodicity) Moreover, the proposed framework takes into account flexible demands and to evaluate different planning strategies to face with these flexible demands Finally, a numerical example highlighting the interest of our approach is given
TL;DR: A localisation algorithm is proposed and also an energy efficient approach that aims to preserve coverage is proposed that locally derives an activity scheduling between nodes with a relatively low cost in terms of energy spent by nodes compared to other approaches.
Abstract: Wireless sensor networks (WSN) constitute a major area of research developing at a very fast pace. Target localisation and coverage are core issues in the field of WSN and represent constraints that affect the effectiveness of WSN. This article focuses on localisation and coverage and identifies a relationship of dependence between the two issues. Throughout this article, a localisation algorithm is proposed and also an energy efficient approach that aims to preserve coverage. The use of a mobile beacon is suggested to divide the region of interest into unit squares following the same method used in the Hilbert space filling curve. A proper choice of the order of the Hilbert curve (i.e. the region subdivisions) is studied to guarantee the localisation of all nodes as well as the total area of coverage. The mobile beacon assists to determine the physical location of undetected nodes by sending beacon packets while traversing the region of interest. It also locally derives an activity scheduling between nodes with a relatively low cost in terms of energy spent by nodes compared to other approaches. In order to validate the effectiveness of the above proposed approach, a series of experiments have been conducted and will be mentioned throughout this article.
TL;DR: The steady availability of the repairable system with preventive maintenance policy is optimized by analyzing the different monotonicity of the failure rate function and discussing the well-posedness of the optimal time interval of executing the preventive maintenance.
Abstract: This article investigates optimal steady availability of a repairable system with six states. Both preventive maintenance and corrective maintenance are considered in this article. By probability argument, the system is described as an abstract Cauchy problem. Using the method of strong continuous semi-group theory, we derive the steady availability of the system. Finally, the optimal time to carry out preventive maintenance is analysed theoretically and numerical examples are presented.
TL;DR: This article provides a study of examining two aspects of supply chain flexibility: order quantity flexibility and lead time flexibility, which have been clarified as the two most common changes which occur in supply chains.
Abstract: Most of the previous literature on production flexibility is centred on the flexibility of manufacturing systems However, the manufacturing system is just one of several key components of a supply chain A supply chain is a network involving all of the activities within individual organisations that link material suppliers, manufacturing factories, distributors, warehouses, retailers and customers Research into the flexibility of a supply chain therefore extends from the intra-organisational flexibilities to the inter-organisational flexibilities This article provides a study of examining two aspects of supply chain flexibility: order quantity flexibility and lead time flexibility, which have been clarified as the two most common changes which occur in supply chains Order quantity flexibility refers to the ability to provide proper order quantity for customer needs Lead time flexibility allows customers to set the order due date depending on their needs A simulation model is built to evaluate the performance on different flexibility levels of a supply chain The experimental results provide interesting insights and can be applied in selecting suppliers with order quantity flexibility and delivery lead time flexibility
TL;DR: A mathematical framework that presents cellular automata as open systems with inputs and outputs is proposed that should help to solve rigorously several problems which have been so far viewed in the context of CA approach but only from a computational point of view as observability, identification or control.
Abstract: The aim of this article is to promote cellular automata (CA) approach for studying control problems on spatially extended systems for which the classical approaches cannot be used. In a similar way as for distributed parameter systems (DPS), this article proposes a mathematical framework that presents CA as open systems with inputs and outputs. This should help to solve rigorously several problems which have been so far viewed in the context of CA approach but only from a computational point of view as observability, identification or control (A. Adamatzky, Identification of cellular Automata Taylor and Francis, 1994; S. El Yacoubi, A. El Jai and N. Ammor, Regional Controllability with Cellular Automata Models, Lecture Notes in Computer Sciences, Springer, 2002, pp. 357-367). The particular controllability problem is studied to illustrate how the proposed formalism could be easily applied. Some simulations for the additive case are given.
TL;DR: In this work, some conditions required to achieve nonnegative output-response are studied and state–feedback and dynamic compensators are constructed to achieve the desired property.
Abstract: Discrete-time linear descriptor systems with restrictions over their trajectory are considered. In this work, some conditions required to achieve nonnegative output-response are studied. State-feedback and dynamic compensators are constructed to achieve the desired property. Finally, some applications to the Leontief economic model are given.
TL;DR: The article presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions’ orders.
Abstract: In this article, the optimisation of the weighting functions for an H∞ controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H∞ controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The article presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions' orders.
TL;DR: In order to find a suitable fuzzy controller, an Iterative Linear Matrix Inequality (ILMI) algorithm is employed in this article to solve the stability conditions for the closed-loop affine T–S fuzzy models.
Abstract: The article considers the analysis and synthesis problem for the discrete nonlinear systems, which are represented by the discrete affine Takagi-Sugeno (T-S) fuzzy models. The state feedback fuzzy controller design methodology is developed to guarantee that the affine T-S fuzzy models achieve Lyapunov stability and strict input passivity. In order to find a suitable fuzzy controller, an Iterative Linear Matrix Inequality (ILMI) algorithm is employed in this article to solve the stability conditions for the closed-loop affine T-S fuzzy models. Finally, the application of the proposed fuzzy controller design methodology is manifested via a numerical example with computer simulations.
TL;DR: The mathematical description of the model uncertainty enabled designing the robust fault detection system, whose effectiveness was verified by the DAMADICS benchmark.
Abstract: This article deals with the problem of determination of the model uncertainty during the system identification via application of the self-organising group method of data handling (GMDH) neural network. In particular, the contribution of the neural network structure errors and the parameter estimates inaccuracy to the model uncertainty were presented. Knowing these sources and applying the Outer Bounding Ellipsoid (OBE) algorithm it was possible to calculate the uncertainty of the parameters and the model output. The mathematical description of the model uncertainty enabled designing the robust fault detection system, whose effectiveness was verified by the DAMADICS benchmark.