Search

The main purpose of the paper is to discuss controllability problems for fractional discrete linear time-varying infinite - dimensional systems. The necessary and sufficient condition for the exact and approximate controllability of the system are proposed and proved. [ABSTRACT FROM AUTHOR]

The hybrid Bi-Conjugate Gradient (BiCG) methods such as the BiCG STABilized (BiCGSTAB), BiCGstab(l) methods and Generalized Product-type method based on BiCG (GPBiCG) are well-known, and the hybrid Bi-Conjugate Residual (BiCR) methods have recently been proposed. It has been reported that a stabilization strategy such that the BiCG coefficients is as accurate as possible is effective for making convergence of the hybrid BiCG methods faster. However, the stabilization strategy for the hybrid BiCR methods has not previously developed. We therefore propose the stabilization strategy for the hybrid BiCR methods. Numerical experiments show that the hybrid BiCR methods with the stabilization strategy are more effective than the original hybrid BiCR methods. [ABSTRACT FROM AUTHOR]

The problem of minimizing of the weighted functional on trajectories of linear time-delay systems with integral constraints on the control is studied. This problem does not have a solution in the class of measurable controls. We have formulated an extended problem in the class of trajectories - functions of bounded variation and substantiated the Cauchy formula for the linear equation with delay in this class of functions. For extended problem we have obtained an expression for the optimal result. The necessary conditions for determining of the optimal control were found. The case of a terminal functional was considered separately. [ABSTRACT FROM AUTHOR]

In this paper, the finite-horizon optimal adaptive control design for linear discrete-time systems with unknown system dynamics by using adaptive dynamic programming (ADP) is presented. In the presence of full state feedback, the terminal state constraint is incorporated in solving the optimal feedback control via the Bellman equation. The optimal regulation of the uncertain linear system is solved in a forward-in-time and online manner without using value and/or policy iterations. Due to the nature of finite horizon, the stability of the closed-loop system is involved but verified by using Lyapunov theory. The effectiveness of the proposed method is verified by simulation results. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to obtain the transfer function of the linear time-invariant system from the feature of step response curve. According to the theory that the typical first-order and second-order component can comprise high order linear system in parallel form, we should firstly sum up laws of various parameters' aspect on the shape of the curve; Secondly, we can use these laws to adjust the value of various parameters in typical component, so as to make the parallel estimation model's step response curve get close to the actual system's step response curve, and we will obtain the mathematical model if the two curves are closed enough. In order to validate the feasibility and correctness of the algorithm, we develop an identification software with the help of MATLAB Graphical User Interface (GUI). The simulation results show that this method is reliable with high accuracy. [ABSTRACT FROM PUBLISHER]

For the purpose of reducing communication rate, we have proposed two types of measurement innovation based scheduling algorithm for state estimation of linear discrete-time stochastic systems in our companion paper. More specifically, the first one is an iterative scheduling algorithm, which sequentially triggers sensor communication during each sampling interval. The second one is simpler and only the vector measurement outside a deadzone will be communicated to estimator. This paper is devoted to stability and performance analysis for the derived optimal estimator under the above schedulers. Necessary and sufficient conditions for stability of the established estimation framework are established. Moreover, it is shown that under a given communication rate, the first scheduling algorithm outperforms the second one. An illustrative example is included to validate our theoretic results. [ABSTRACT FROM PUBLISHER]

The optimal performance under the effect of the plant uncertainty is investigated in this paper. The plants with the relative degree zero and one considered are unstable. The uncertain disturbance problem is formulated by utilizing stochastic embedding of the uncertainty. The expectation of the H2 norm for the weighted tracking sensitivity error between the nominal system and the true system is defined and minimized by adjusting the true controller. The optimal redesign controller are derived. One example is given to evaluate the optimal weighted performance. It is shown that it is hard to control the system as the unmodeled gain increases. [ABSTRACT FROM PUBLISHER]

Based on the inverse system, a control synthesis method is presented for the SISO linear continuous system in time domain. Several initial conditions to achieve the ideal inverse system control in special state-space model is deduced, and verified by simulation. The result shows that the system initial state has effect on its performance. Set the initial state of the controller as adjustable parameters, then the impact on the initial state of the object can be offset. Therefore, the command signal can be completely tracked by the system output, thus gives the ideal control. [ABSTRACT FROM PUBLISHER]

This note gives a brief survey on discrete-time stochastic iterative learning control (SILC) from three aspects, namely, SILC for linear system, nonlinear system and system with other stochastic signal. Two major approaches, stochastic Kalman filtering approach and stochastic approximation approach, for SILC are proposed. Some open questions are also included. [ABSTRACT FROM PUBLISHER]

This paper proposes an estimation framework under scheduled measurements for linear discrete-time stochastic systems. Both controllable and uncontrollable schedulers are considered. Under a controllable scheduler, only the normalized measurement innovation greater than a threshold will be communicated to the estimator. While under an uncontrollable scheduler, the time duration between consecutive sensor communications is triggered by an independent and identically distributed process. For both types of scheduler, recursive estimators that achieve the minimum mean square estimation error are derived, respectively. Moreover, necessary and sufficient conditions for stability of the mean square estimation error are provided. [ABSTRACT FROM PUBLISHER]

A fundamental problem in the development and validation of faults detection and diagnoses is to obtain fault signature data and if is possible to be able to configure fault parameters. [ABSTRACT FROM PUBLISHER]

This paper investigates the reduced-order design of large-scale discrete-time linear systems and discusses its applications like the design of observer-based reduced order controllers for the stabilization of large scale linear discrete-time control systems. The reduced order for a given linear plant is obtained using the dominant state of the linear plant. Using this reduced-order linear model, sufficient conditions are derived for the design of observer-based reduced order controllers. A separation principle has been established in this paper which demonstrates that the observer poles and controller poles can be separated and hence the pole-placement problem and observer design are independent of each other. [ABSTRACT FROM PUBLISHER]

Investigating positivity of polynomials over the complex unit disc is a relevant problem in electrical and computer engineering. This paper provides two sufficient and necessary conditions for solving this problem via linear matrix inequalities (LMIs). These conditions are obtained by exploiting trigonometric transformations, a key tool for the representation of polynomials, and results from the theory of positive polynomials. Some numerical examples illustrate the proposed conditions. [ABSTRACT FROM PUBLISHER]

In order to design an optimal controller for a feedback system with an unknown plant, it is a common way to identify the plant by performing plural experiments. On the contrary, certain methods used to design a controller using the data from one-time experiments have been proposed. Such methods include Virtual Reference Feedback Tuning (VRFT) and Noniterative Correlation-based Tuning (NCbT). These methods are expected to reduce the design cost. However, VRFT cannot be used to design an optimal controller from a data with noise and NCbT is limited in that there is no correlation of a reference signal with noise. An actual plant often has higher harmonics of an input signal as noise. Such noises are correlated with a reference signal. Therefore, a controller design must be able to deal with various kinds of noises. This paper proposes a method of applying spline fitting to VRFT. To verify the effectiveness of this technique, we compared the response of the proposed method with that of VRFT and NCbT on a simulator. Then, we designed each controller using VRFT, NCbT, and VRFT+SF, respectively, by providing white Gaussian noise and periodic noise. As a result, this paper shows that the proposed method surpasses the original VRFT and NCbT based on overall experimental results. [ABSTRACT FROM PUBLISHER]

Several methods have been proposed for designing a controller using experimental data directly without identifying a controlled plant. Among them, optimal controller design methods from one-time experimental data, such as Virtual Reference Feedback Tuning (VRFT), Fictitious Reference Iterative Tuning (FRIT), and Noniterative Correlation-based Tuning (NCbT), are especially expected to reduce the time and cost of designing a controller. VRFT and FRIT make a reference signal from experimental data. In contrast, NCbT is a method that removes noise influence by determining parameters for the controller so as not to have the interrelation of the correlation function between a reference signal and noise. However, VRFT and FRIT cannot deal with data that include noises. Although NCbT can be used to design an optimal controller from experimental data with noise, it is limited to cases where there is no interrelation between a reference signal and noise. In this paper, we show a summary of the conventional method and argue about a problem where there is noise. We also propose a data conversion method using linearity after applying spline fitting instead of using experimental data directly. In addition, we discuss the advantages of the conventional method and the proposed method. [ABSTRACT FROM PUBLISHER]

The 1980 IDR method plays an important role in the history of Krylov subspace methods. It started the research of transpose-free Krylov subspace methods. In this paper, we attempt to bring back A-transpose to the research by presenting a new ML(n)BiCGStab algorithm named ML(n)BiCGStabt. ML(n)BiCGStabt involves A-transpose in its implementation. Comparisons of this new algorithm with the existing ML(n)BiCGStab algorithms will be made. [ABSTRACT FROM PUBLISHER]

Listening to a lecture in a classroom is a common process for studying in Thailand. Ability of learning is affected by the ability of hearing the instructors' speech. Acoustical environments of the classroom, hence, can influence speech intelligibility. In this research, acoustical parameters, reverberation time, listeners' locations in classrooms and their effects were studied. By using an assumption of linear system of the classrooms, the room reverberation, background noise, and other classroom acoustical factors can be implicated as an impulse response of the system. Maximum length sequence was used to identify the impulse responses at listeners' locations, which are at front, middle, and rear section of the classrooms. A clean speech, recorded in a semi-anechoic room, was convoluted with a series of the measured classrooms' impulse responses to yield a set of simulated reverberant speeches that the listener at each location in the classes would have heard. A number of volunteers were invited to test an ability of understanding the speech. The results showed that the reverberation and the listeners' location severely affected the speech intelligibility. Students sitting at front of the classes would gain better speech clarity than those sitting at middle or rear parts of the classes. Location of listening at back section, closed to the rear solid walls in the classrooms with bad reverberation times was the worse location for gaining speech intelligibility. [ABSTRACT FROM PUBLISHER]

This paper review the effectiveness of the parity space approach to identify faults or disturbance in a system. The most commonly used is the observer based procedures, and redundancy relationship method. This involves analytical mathematical analysis of geometry and bilinear algebra. Then, technological advances which require complex computation such as artificial intelligence and genetic algorithm had made tremendous improvement to fault Detection and Isolation (FDI) analysis. Dynamic Parity Space Approach was studied for a discrete state-space model. Important data will be extracted using this approach especially for residual generation which is the backbones of FDI analysis. Subsequently, at each time instant k, the generated residuals will form a matrix that will define the fault signature. It is remarkable that this approach is proven in this study to be effective in diagnosis and faults isolation. [ABSTRACT FROM PUBLISHER]

In this paper, the authors describe the parallel implementation of a conjugate gradient method in a many-core system specially for solving the sparse linear systems. The new version of algorithm implementation differs from the one applied earlier [1], because it uses a special method for storing sparse coefficient matrices: only non-zero elements are stored and taken into account during computations, so that the sparsity of the coefficient matrix is taken full advantage of. Finally, A speedup of the parallel algorithm has been examined for different coefficient sparse matrices resulting in solving different physical problems. [ABSTRACT FROM PUBLISHER]

The Generalized Conjugate Residual (GCR) method with a variable preconditioning is an efficient method for solving a large sparse linear system Ax = b. It has been clarified by some numerical experiments that the Successive Over Relaxation (SOR) method is more effective than Krylov subspace methods such as GCR and ILU(0) preconditioned GCR for performing the variable preconditioning. However, SOR cannot be applied for performing the variable preconditioning, when solving the singular linear system, where the coefficient matrix has diagonal elements of 0 or is rectangular matrix. Therefore, we reconsider the splitting of A, and propose a variant of SOR. By numerical experiments on the singular linear system, we show the efficiency of the variable preconditioned GCR method using the variant of SOR. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the restrictive Padé-type approximation by combining the ideas of Padé-type approximation and restrictive Padé approximation. It is highly accurate and gives exact values at some given points. A mixed model reduction method of linear system, that is, the restrictive Padé-type-Routh method is presented for retaining the stability of the original high-order linear system. Two examples illustrating the proposed method are provided. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the controllability problem of linear systems from a graph-theoretic perspective. Our aim is to characterize the interconnection graph of a controllable linear system and discuss the relationship between controllability and connectivity. We have derived sufficient and necessary conditions for controllability and connectivity using eigenvalues and eigenvectors of the graph. In particular, we prove that for a regular interconnection graph, controllability implies connectivity. [ABSTRACT FROM PUBLISHER]

Lunbeger Descend-Order-Observers Design Requires Full Rank of Output Matrix. It Is Given Impetus to General for non-Zero-Rank. Correspondence Descend Order Observers Are Design. Principle, Method and Step Are Given. They Are Compact and Can Be Done Easily. [ABSTRACT FROM PUBLISHER]

A simple adaptive frequency sampling scheme employing interval bisection combined with the Cauchy method implemented through Stöer-Bulirsch algorithm is examined as a tool for interpolating wideband system responses from numerical electromagnetic (EM) simulation. Emphasis is placed on examining the convergence of the method. Numerical experiments show that reliable interpolation can be done at substantially reduced computational effort compared to that of conventional uniform sampling. The relevant procedures can be easily interfaced with existing EM simulators. [ABSTRACT FROM PUBLISHER]