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?     

The output feedback control for uncertain nonholonomic systems

This paper considers the problems of almost asymptotic stabilization and global asymptotic regulation （GAR） by output feedback for a class of uncertain nonholonomic systems. By combining the nonsmooth change of coordinates and output feedback domination design together, we construct a simple linear time-varying output feedback controller, which can universally stabilize a whole family of uncertain nonholonomic systems. The simulation demonstrates the effectiveness of the proposed controller.     

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 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.     

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.     

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.     

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.     

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.     

Robust H ∞ output feedback control with pole placement constraints for uncertain discrete-time fuzzy systems

This paper investigates the problem of multi-objective control for a class of uncertain discrete-time fuzzy systems. The state-space Takagi–Sugeno T–S fuzzy model with linear fractional parameter uncertainties is adopted. Based on a linear matrix inequality approach and via so-called dynamic parallel distributed compensation, a fuzzy full-order dynamic output feedback controller is developed such that the L ₂ gain performance from the exogenous input signals to the controlled output is less than or equal to some prescribed value and, for all admissible uncertainties, the closed-loop poles of each local system are within a pre-specified sub-region of complex plane. Two numerical examples are provided to illustrate the effectiveness of the proposed design method.     

Bang–bang property for an uncertain saddle point problem

In this paper, we propose a bang–bang control model for a saddle point problem using the optimistic value criterion. By using equation of optimality in uncertain optimal control, a bang–bang control problem is investigated. And then, an example is given to illustrate our results.     

New synthesis algorithm of static output feedback sliding mode control for a class of uncertain systems

Based on a kind of regular form,a Lyapunov matrix with special structure is presented to design the sliding surface matrix conveniently and then an effective algorithm is developed on it. A simple static output feedback sliding mode control law without extra dynamic equation is given, such that the predefined shding surface is reached in finite time for the general matching uncertainties. In the reported result, this extra dynamic equation is used for evaluating the norm bound of the unmeasured state vector. Finally, some examples are studied to illustrate the proposed approach.