Input–output structure and growth in China

The fast and steady economic growth in China during the 1990s has attracted much international attention. Using the three most recent Chinese input–output tables, this paper investigates industry structure and inter-industry relationships and the relationship of both to economic growth. The input–output tables contain intermediate demand and final demand for six broad industries, namely, Agriculture, Industry, Construction, Transportation, Post and Telecommunications, Services, and Others, for 1992, 1995 and 1997, which enables computing of input–output coefficients for three time periods. As direct and indirect input–output coefficients characterise industry structure during a particular time period, changes over time reflect the patterns in industry structure evolvement. Furthermore, output growth in a particular industry can be analysed from two different sources, namely the changes in input–output coefficients that reflect technological change, and the change in final demand. This paper sheds light on four different issues over the five-year period from 1992 to 1997: (1) Was growth driven by technological changes or final demand increases? (2) As a result of the interdependence of industries, how did an increase in final demand in one industry affect growth in another? (3) How has the bottleneck of an insufficient capability in the transportation, post and telecommunications sectors to cope with demands from other sectors been affected during this period? (4) Has the industry structure of the economy been shifting in conformity with traditional growth theory, namely, with a decline in the agricultural sector and a rise in the modern industrial sector?     

Input–output data filtering based recursive least squares identification for CARARMA systems

This paper uses an estimated noise transfer function to filter the input–output data and presents filtering based recursive least squares algorithms (F-RLS) for controlled autoregressive autoregressive moving average (CARARMA) systems. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the recursive least squares method can be used to estimate the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed F-RLS algorithm has a high computational efficiency because the dimensions of its covariance matrices become small and can generate more accurate parameter estimation compared with other existing algorithms.     

Input–output identification of controlled discrete manufacturing systems

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.     

Input–output decoupling by output feedback with disturbance attenuation of H∞ control for any square system

The combined problem of diagonal decoupling by output feedback with disturbance attenuation performance for any square system is formulated. A sufficient condition which is more general than the necessary and sufficient one of static state feedback for the existence of the controller is determined. The necessary and sufficient conditions for the controller and decoupled system that are both stable are presented. An analytical form and a numerical computable method for the controller are obtained for the first time when the condition is satisfied. The correctness of our method is tested by simulations for an example.     

Input–output finite-time stability of discrete-time systems under finite-time boundedness

This paper deals with the input–output finite-time stability of discrete time-varying linear systems in the presence of finite-time boundedness. The state boundedness and output stability of this system are concerned simultaneously to avoid the large unacceptable values during certain transients. For two different classes of norm-bounded input signals, the sufficient conditions for the system satisfying both the state finite-time boundedness (FTB) and input–output finite-time stability (IO-FTS) are developed. Based on a set of linear matrix inequalities (LMIs), the controller design method via state feedback for the discrete-time system satisfying both FTB and IO-FTS is presented for the two classes of external norm-bounded input. The conditions proposed can simultaneously guarantee the state and output of closed-loop system do not exceed the boundary during the specified finite-time interval. Two examples are employed to verify the effectiveness of the proposed method.     

Input–output operability of control systems: The steady-state case

For high-dimensional systems with more outputs than inputs, some outputs must be controlled within ranges, instead of at set-points. This may also be true if the outputs are equal in number to the inputs and disturbances of high magnitude exist. A linear programming framework is postulated to calculate the tightest achievable operating ranges of the outputs, given the ranges of the inputs and the expected disturbances, for any linear input–output control system at the steady-state. This approach removes the computational constraints on the size of the problem that a previous communication of the authors [1] could address. The hyper-volume obtained for the tightest achievable outputs’ region of a high-dimensional industrial process is calculated to be four orders of magnitude smaller than the one initially assumed, enabling much tighter control.     

Input–output finite-time stabilisation of nonlinear stochastic system with missing measurements

This paper considers the problem of the input–output finite-time stabilisation for a class of nonlinear stochastic system with state-dependent noise. The phenomenon of the missing measurements may occur when state signals are transmitted via communication networks. An estimating method is proposed to compensate the lost state information. And then, a compensator-based controller is designed to ensure the input–output finite-time stochastic stability (IO-FTSS) of the closed-loop system. Some parameters-dependent sufficient conditions are derived and the corresponding solving approach is given. Finally, numerical simulations are provided to demonstrate the feasibility and effectiveness of the developed IO-FTSS scheme.     

Passivity analysis for uncertain BAM neural networks with time delays and reaction–diffusions

This article deals with the problem of passivity analysis for delayed reaction–diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.     

Non-fragile H∞ dynamic output feedback control for uncertain Takagi–Sugeno fuzzy systems with time-varying delay

This paper mainly focuses on the problem of non-fragile H_∞ dynamic output feedback control for a class of uncertain Takagi–Sugeno fuzzy systems with time-varying state delay. Based on a new type of Lyapunov–Krasovskii functional without ignoring any subtle integral terms in the derivatives, a less conservative dynamic output feedback controller with additive gain variations is designed, which guarantees that the closed-loop fuzzy system is asymptotically stable and satisfies a prescribed H_∞-performance level. Furthermore, the obtained parameter-dependent conditions are given in terms of solution to a set of linear matrix inequalities, which improve some existing relevant results. Finally, numerical examples are given to illustrate the effectiveness and merits of the proposed method.     

Practical design of feedback systems with uncertain multivariable plants†

A previous paper was devoted to the rigorous development of an exact synthesis technique for uncertain multiple-input-output feedback systems. This paper is devoted to practical design execution, by means of two detailed 2×2 examples with large plant parameter uncertainty. The outstanding features are : (1) The problem muat be quantitative. Performance bounds must be assigned to the matrix of closed-loop. transfer functions, and the parameter uncertainty sets defined. (2) The design problem is translated into separate quantitative single-loop systems such with i1« own sets of parameter uncertainty, disturbances and performance tolerances. The solutions (compensation functions) for the single-loop problems are guaranteed to solve the multiple-input-output problem. There is no need to consider the characteristic equation of the latter or any of its other system functions. (3) There is considerable design transparency. As he proceeds, the designer can see the trade-offs between the loops, and may, if desirable, sacrifice one for the other. Also he may modify the original performance tolerances, without violating them, so as to economize on the compensation function band widths. (4) The design is tuned to the problem. The larger the uncertainty and the more narrow the tolerances, the larger the required compensation bandwidths. In the case of no uncertainty, the design emerges with no need for feedback at all.     

Input–output finite-time mean square stabilisation of stochastic systems with Markovian jump

The notion of input–output finite-time mean square (IO-FTMS) stability is introduced for Itô-type stochastic systems with Markovian jump parameters. Concerning a class of random input signals W, sufficient conditions are presented for the IO-FTMS stability and stabilisation of stochastic nonlinear Markov jump systems in terms of coupled Hamilton–Jacobi inequalities. When specialising to the linear case, these criteria are turned into coupled linear matrix inequalities. Moreover, the quadratic IO-FTMS stabilisation is addressed when polytopic uncertainty appears in the transition rate. Finally, a numerical example with simulations is exploited to illustrate the proposed techniques.     

Input–output feedback linearizing control of linear induction motor taking into consideration the end-effects. Part I: Theoretical analysis

This first part of a paper, divided into two parts, deals with the theoretical formulation of the input–output feedback linearization (FL) control technique as to be applied to linear induction motors (LIMs). Linear induction motors, differently from rotating induction motors (RIMs), present other strong non-linearities caused by the so-called dynamic end effects, leading to a space-vector model with time-varying inductance and resistance terms and an additional braking force term. This paper, starting from a dynamic model of the LIM taking into consideration its dynamic end effects, previously developed by the same authors, defines a feedback linearization (FL) technique suited for LIMs, since it inherently considers its end effects. It further emphasizes the role of the LIM dynamic end effects in the LIM control formulation, highlighting the differences with respect to the corresponding technique for RIMs. It describes the control design criteria, taking also into consideration the constraints on the control and controlled variables, arising from the application of such control technique in a real scenario.The second part of this paper describes the set of tests, both in numerical simulations and experiments, performed to assess the correctness of the proposed control technique.     

Faà di Bruno Hopf algebra of the output feedback group for multivariable Fliess operators

Given two nonlinear input–output systems written in terms of Chen–Fliess functional expansions, i.e., Fliess operators, it is known that the feedback interconnected system is always well defined and in the same class. An explicit formula for the generating series of a single-input, single-output closed-loop system was provided by the first two authors in earlier work via Hopf algebra methods. This paper is a sequel. It has four main innovations. First, the full multivariable extension of the theory is presented. Next, a major simplification of the basic setup is introduced using a new type of grading that has recently appeared in the literature. This grading also facilitates a fully recursive algorithm to compute the antipode of the Hopf algebra of the output feedback group, and thus, the corresponding feedback product can be computed much more efficiently. The final innovation is an improved convergence analysis of the antipode operation, namely, the radius of convergence of the antipode is computed.     

Input–output method to fault detection for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses

The fault detection (FD) problem for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses is investigated in this paper. The communication links between the plant and the FD filter (FDF) are assumed to be imperfect, and the missing probability is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [0 1]. The discrete-time delayed fuzzy networked system is first transformed into the form of interconnect ion of two subsystems by applying an input–output method and a two-term approximation approach, which are employed to approximate the time-varying delay. Our attention is focused on the design of fuzzy FDF (FFDF) such that, for all data missing conditions, the overall FD dynamics are input–output stable in mean square and preserves a guaranteed performance. Sufficient conditions are first established via H_∞ performance analysis for the existence of the desired FFDF; meanwhile, the corresponding solvability conditions for the desired FFDF gains are characterised in terms of the feasibility of a convex optimisation problem. Moreover, we show that the obtained criteria based on the input–output approach can also be established by applying the direct Lyapunov method to the original time-delay systems. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.     

Supply-side inoperability input–output model (SIIM) for risk analysis in manufacturing systems

This paper proposes a methodological approach in risk analysis of interdependent manufacturing systems which is based on Ghosh model - a variation of the Leontief input-output model. The proposed approach is able to quantify the impact of supply perturbations in a manufacturing system in terms of cost-price increase in production output due to increase in prices of value-added input brought about by degraded supply resulting from natural or man-made disasters and sudden policy changes. Unlike demand-driven perturbation models presented in literature, the supply-driven inoperability input-output model (SIIM) appears to be more relevant particularly in make-to-order manufacturing systems as demand is usually pre-determined and production costs typically increase when prices of inputs increase. An actual case study was carried out in a furniture manufacturing firm in central Philippines and three scenarios were presented to illustrate the proposed approach: (1) a sudden log ban in the location of the supplier, (2) increase in labor costs and (3) metal shortage caused by severe weather condition. Results show that supply perturbation of upstream processes does not impact downstream processes as long as these processes remain independent of the perturbed upstream process as described in firm's system structure and topology. This also shows that the magnitude of impact of non-perturbed process depends on its nature of interdependence of the perturbed process. The proposed approach is highly relevant for manufacturing practitioners in formulating and implementing mitigation and adaptation policies.     

The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model

In this paper, an innovatory modelling framework is proposed to conduct the uncertainty recovery analysis for the interdependent infrastructure sectors based on the dynamic inoperability input–output model (DIIM). The DIIM captures the inoperability of infrastructure systems, and therefore can easily analyse how perturbations propagate among interconnected infrastructures and how to implement effective mitigation efforts after a disaster. In this paper, based on the random recovery time distribution, we apply the Monte Carlo simulation to obtain the distributions of the economic losses for the critical interdependent infrastructure sectors after a disaster. The proposed method can provide the decision-makers the guidance in making suitable risk-management decisions as well as how the risks can be mitigated, if the disaster cannot be avoided to happen in the first place.     

Model predictive control for uncertain max–min-plus-scaling systems

In this paper we extend the classical min–max model predictive control framework to a class of uncertain discrete event systems that can be modelled using the operations maximization, minimization, addition and scalar multiplication, and that we call max–min-plus-scaling (MMPS) systems. Provided that the stage cost is an MMPS expression and considering only linear input constraints then the open-loop min–max model predictive control problem for MMPS systems can be transformed into a sequence of linear programming problems. Hence, the min–max model predictive control problem for MMPS systems can be solved efficiently, despite the fact that the system is non-linear. A min–max feedback model predictive control approach using disturbance feedback policies is also presented, which leads to improved performance compared to the open-loop approach.