ON EVALUATING THE RUN LENGTH PROPERTIES OF X^— CHARTS WITH ESTIMATED CONTROL LIM

X＾－ charts with estimated coutrol limits are commonly used in practice and treated as if the in-control process parameters were known.However,the former can behave quite differently from the latter.To understand the differences,it is necessary to study the run length distribution(RLD),its mean(ARL)and standard deviation(SDRL) of the X^- charts when the control limits are estimated.However,ARL and SDRL are integrals over and infinite region with a boundless integrand,the finiteness has not been proved in literature.In this paper,we show the finiteness and uniform integrability of ARL and SDRL.Furthermore,we numerically evaluate the ARL,SDRL and the RLD using number theory method.A numerical study is conducted to assess the performance of the proposed method and the results are compared with those given by Quesenberry and Chen.     

A framework of setting up goal control limits of target costing for non-normal distributions

This study provides a framework of target costing to extend its original scope when the underlying distribution is non-normal. The new specification limits can be derived by listening to the market price from Taguchi loss function. Later, the new specification limits can be linked through the non-normality-based C^^pk value along with non-normality-based X^^-Rcontrol charts to derive goal control limits. Moreover, an example is provided to illustrate the usefulness of the proposed framework of target costing by relentlessly reducing cost and improving product quality to gain competitiveness in the marketplace.     

Research on improved integrated FDD/FTC with effectiveness factors

This paper investigates the integrated fault detection and diagnosis(FDD) with fault tolerant control(FTC) method of the control system with recoverable faults.Firstly,a quasi-linear parameter-varying(QLPV) model is set up,in which effectiveness factors are modeled as time-varying parameters to quantify actuators and sensors faults.Based on the certainty equivalency principle,replacing the real time states in the nonlinear term of the QLPV model with the estimated states,the parameters and states can be estimated by a two-stage Kalman filtering algorithm.Then,a polynomial eigenstructure assignment(PEA) controller with time-varying parameters and states is designed to guarantee the performance of the system with recoverable faults.Finally,mathematical simulation is performed to validate the solution in a satellite closed-loop attitude control system,and simulation results show that the solution is fast and effective for on-orbit real-time computation.     

Intelligent control over chaos in the rolling process and its comparison with tracking control

Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.     

SAR images classification method based on Dempster-Shafer theory and kernel estimate

To study the scene classification in the Synthetic Aperture Radar (SAR) image, a novel method based on kernel estimate, with the Markov context and Dempster-Shafer evidence theory is proposed. Initially, a nonparametric Probability Density Function (PDF) estimate method is introduced, to describe the scene of SAR images. And then under the Markov context, both the determinate PDF and the kernel estimate method are adopted respectively, to form a primary classification. Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification. Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification. Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results. Experimental results on real SAR images illustrate a rather impressive performance.     

Adaptive Robust Dead-Zone Compensation Control of Electro-Hydraulic Servo Systems with Load Disturbance Rejection

A backstepping method based adaptive robust dead-zone compensation controller is proposed for the electro-hydraulic servo systems(EHSSs) with unknown dead-zone and uncertain system parameters.Variable load is seen as a sum of a constant and a variable part.The constant part is regarded as a parameter of the system to be estimated real time.The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them.Compared with the traditional dead-zone compensation method,a dead-zone compensator is incorporated in the EHSS without constructing a dead-zone inverse.Combining backstepping method,an adaptive robust controller(ARC) with dead-zone compensation is formed.An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure.Simulations show that the proposed method has a splendid tracking performance,all the uncertain parameters can be estimated,and the disturbance has been rejected while the dead-zone term is well estimated and compensated.     

Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.     

Adaptive fault-tolerant controller design for airbreathing hypersonic vehicle with input saturation

The problem of fault-tolerant control is discussed for the longitudinal model of an airbreathing hypersonic vehicle(AHV) with actuator faults and external disturbances.Firstly,a fault-tolerant control strategy is presented for the longitudinal model of an AHV,which guarantees that velocity and altitude track their reference trajectories at an exponential convergence rate.However,this method needs to know the minimum value of the actuator efficiency factor and the upper bound of the external disturbances,which makes it not easy to implement.Then an improved adaptive fault-tolerant control scheme is proposed,where two adaptive laws are employed to estimate the upper bound of the external disturbances and the minimum value of the actuator efficiency factor,respectively.Secondly,the problem of designing a control scheme with control constraints is further considered,and a new adaptive fault-tolerant control strategy with input saturation is designed to guarantee that velocity and altitude track their reference trajectories.Finally,simulation results are given to show the effectiveness of the proposed methods.     

Multi-target localization for bistatic MIMO radar in the presence of unknown mutual coupling

A decoupling-estimation signal parameters via rotarional invariance technique(ESPRIT) method is presented for multi-target localization with unknown mutual coupling in bistatic multiple-input multiple-output(MIMO) radar.Two steps are carried out in this method.The decoupling operation between angle and mutual coupling estimates is realized by choosing the auxiliary elements on both sides of the transmit and receive uniform linear arrays(ULAs).Then the ESPRIT method is resilient against the unknown mutual coupling matrix(MCM) and can be directly utilized to estimate the direction of departure(DOD) and the direction of arrival(DOA).Moreover,the mutual coupling coefficient is estimated by finding the solution of the linear constrained optimization problem.The proposed method allows an efficient DOD and DOA estimates with automatic pairing.Simulation results are presented to verify the effectiveness of the proposed method.     

High-end equipment development task decomposition and scheme selection method

Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.     

Low complexity DOA estimation for massive UCA with single snapshot

In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector.Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account.Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares(LS) without any complex searches.In addition, the refinement can be iteratively implemented to enhance the estimation results.Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity.Numerical simulations are presented to demonstrate the effectiveness of the proposed method.     

THE RANKINGS OF CONTROL SYSTEMS AND APPLICATIONS

The notion of ranking was initiated into control theory by Glad a few yearsago. In this paper we mainly discuss one kind of rankings and study its invarianceproperties. This kind of rankings is closely related to the structure algorithm. The relationsbetween invariants of this kind of rankings and the invariants given by other studies, e.g.the invertibility indices of a system and so on, are examined. As applications of rankings,we deal with some well-known problems in "ranking technique". In this paper we mainlyintroduce a new method rather than solve new problems.     

Stability Analysis of Event-Triggered Networked Control Systems with Time-Varying Delay and Packet Loss

Networked control systems(NCSs) are facing a great challenge from the limitation of network communication resources. Event-triggered control(ETC) is often used to reduce the amount of communication while still keeping a satisfactory performance of the system, by transmitting the measurements only when an event-triggered condition is satisfied. However, some network-induced problems would happen inevitably, such as communication delay and packet loss, which can degrade the control performance significantly and can even lead to instability. In this paper, a periodic eventtriggered NCS considering both time-varying delay and packet loss is studied. The system is discretized into a piecewise linear system with uncertainty. Then the model is handled by a polytopic overapproximation method to be more suitable for stability analysis. Finally, stability conditions are obtained and presented in terms of linear matrix inequalities(LMIs). The result is illustrated by a numerical example.     

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.     

Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints

This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs) with state-dependent uncertain switching and control constraints. First, based on the properties of the semi-tensor product of matrices and the vector representation of logic, an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form. Second, an input transformation and a switching model are constructed to transfer the original SLCN into one with a...     

Robust bounded control for uncertain flight dynamics using disturbance observer

The robust bounded flight control scheme is developed for the uncertain longitudinal flight dynamics of the fighter with control input saturation invoking the backstepping technique. To enhance the disturbance rejection ability of the robust flight control for fighters, the sliding mode disturbance observer is designed to estimate the compounded disturbance including the unknown external disturbance and the effect of the control input saturation. Based on the backstepping technique and the compounded disturbance estimated output, the robust bounded flight control scheme is proposed for the fighter with the unknown external disturbance and the control input saturation. The closed-loop system stability under the developed robust bounded flight control scheme is rigorously proved using the Lyapunov method and the uniformly asymptotical convergences of all closed-loop signals are guaranteed. Finally, simulation results are presented to show the effectiveness of the proposed robust bounded flight control scheme for the uncertain longitudinal flight dynamics of the fighter.     

Impact of Different Forms of Interest Rate Differential on the Flexible Price Monetary Model

In this paper, in order to investigate whether the impact of different forms of interest rate differential may pass on to the flexible price monetary model, two flexible price monetary models, which are separately derived from the generalized monetary models with log-level interest rate differential and that with interest rate differential, are tested for China yuan to US dollar exchange rate. Through Johansen maximum likelihood method, we find that there is little support in the cointegrating coefllcient estimates for both flexible price monetary models for yuan/dollar exchange rate. However, the latter is generally better than the former in the light of sum of squared residual and log likelihood statistics. Therefore, we conclude that there is no transitive impact of different forms of interest rate differential on the flexible price monetary model.     

ESTIMATION OF CHAIN REACTION BANKRUPTCY STRUCTURE BY CHANCE DISCOVERY METHO—WITH TIME ORDER METHOD AND DIRECTED KEYGRAPH

Chain reaction bankruptcy is regarded as common phenomenon and its effect is to be taken into account when credit risk portfolio is analyzed.But consideration and modeling of its effect leave much room for improvement.That is mainly because method for grasping relations among companies with limited data is underdeveloped.In this article,chance discovery method is applied to estimate industrial relations that are to include companies' relations that transmit chain reaction of bankruptcy. Time order method and directed KeyGraph are newly introduced to distinguish and express the time order among defaults that is essential information for the analysis of chain reaction bankruptcy.The steps for the data analysis are introduced and result of example analysis with default data in Kyushu, Japan,2005 is presented.The structure estimated by the new method is compared with the structure of actual account receivable holders of bankrupted companies for evaluation.     

Estimation of chain reaction bankruptcy structure by chance discovery method — With time order method and directed KeyGraph

Chain reaction bankruptcy is regarded as common phenomenon and its effect is to be taken into account when credit risk portfolio is analyzed. But consideration and modeling of its effect leave much room for improvement. That is mainly because method for grasping relations among companies with limited data is underdeveloped. In this article, chance discovery method is applied to estimate industrial relations that are to include companies＇ relations that transmit chain reaction of bankruptcy. Time order method and directed KeyGraph are newly introduced to distinguish and express the time order among defaults that is essential information for the analysis of chain reaction bankruptcy. The steps for the data analysis are introduced and result of example analysis with default data in Kyushu, Japan, 2005 is presented. The structure estimated by the new method is compared with the structure of actual account receivable holders of bankrupted companies for evaluation.