Data-driven asymptotic stabilization for discrete-time nonlinear systems

In this paper, we propose a data-driven feedback controller design method based on Lyapunov approach, which can guarantee the asymptotic stability of the closed-loop and enlarge the estimate of domain of attraction (DOA) for the closed-loop. First, sufficient conditions for a feedback controller asymptotically stabilizing the discrete-time nonlinear plant are proposed. That is, if a feedback controller belongs to an open set consisting of pairs of control input and state, whose elements can make the difference of a control Lyapunov function (CLF) to be negative-definite, then the controller asymptotically stabilizes the plant. Then, for a given CLF candidate, an algorithm, to estimate the open set only using data, is proposed. With the estimate, it is checked whether the candidate is or is not a CLF. If it is, a feedback controller is designed just using data, which satisfies sufficient conditions mentioned above. Finally, the estimate of DOA for closed-loop is enlarged by finding an appropriate CLF from a CLF candidate set based on data. Because the controller is designed directly from data, complexity in building the model and modeling error are avoided.     

Data-driven realizations of kernel and image representations and their application to fault detection and control system design

This paper deals with the data-driven design of observer-based fault detection and control systems. We first introduce the definitions of the data-driven forms of kernel and image representations. It is followed by the study of their identification. In the context of a fault-tolerant architecture, the design of observer-based fault detection, feed-forward and feedback control systems are addressed based on the data-driven realization of the kernel and image representations. Finally, the main results are demonstrated on the laboratory continuous stirred tank heater (CSTH) system.     

基于数据驱动的航站楼碳浓度控制系统设计及应用仿真

碳浓度偏高是导致航站楼内空气质量不佳的主要原因之一,且航站楼内随旅客数量变化碳浓度呈现的非平稳性给其控制带了挑战.为了营造航站楼内良好的空气质量,本文设计基于数据驱动的碳浓度控制系统,该系统根据航站楼内碳浓度、新风量等环境数据组成的信息向量在基础数据库中选取邻元素,通过参数调节器对邻元素的PID参数进行有向学习修正,实现系统PID参数在线优化.利用MATLAB/Simulink平台根据航站楼实际环境建立碳浓度控制系统进行应用仿真,结果表明:该系统通过参数在线优化实现了非平稳碳浓度的良好控制,可保证航站楼内碳浓度低于800ppm,系统稳态误差小于1%,与定风量系统相比节约风量约30%.该系统在为旅客提供良好的空气质量的同时可为航站楼节能设计提供参考.     

Data Driven Order Selection for Projection Estimator of the Spectral Density of Time Series with Long Range Dependence

Fractional exponential (FEXP) models have been introduced by Robinson (1991) and Beran (1993) to model the spectral density of a covariance stationary long-range dependent process. In this class of models, the spectral density f ( x ) of the process is decomposed as f ( x ) = |1 − exp( ix )|^{−2 d} f _*( x ), where f _*( x ) accounts for the short-memory component. In this contribution, FEXP models are used to construct semi-parametric estimates of the fractional differencing coefficient and of the spectral density, by considering an infinite Fourier series expansion of log f _*( x ). A data-driven order selection procedure, adapted from the Mallows' C _p procedure, is proposed to determine the order of truncation. The optimality of the data-driven procedure is established, under mild assumptions on the short-memory component f _*( x ). A limited Monte-Carlo experiment is presented to support our claims.     

Towards the robotic characterization of the constitutive response of composite materials

A historical and technical overview of a paradigm for automating research procedures on the area of constitutive identification of composite materials is presented. Computationally controlled robotic, multiple degree-of-freedom mechatronic systems are used to accelerate the rate of performing data-collecting experiments along loading paths defined in multidimensional loading spaces. The collected data are utilized for the inexpensive data-driven determination of bulk material non-linear constitutive behavior models as a consequence of generalized loading through parameter identification/estimation methodologies based on the inverse approach. The concept of the dissipated energy density is utilized as the representative encapsulation of the non-linear part of the constitutive response that is responsible for the irreversible character of the overall behavior. Demonstrations of this paradigm are given for the cases of polymer matrix composite materials systems. Finally, this computational and mechatronic infrastructure is used to create conceptual, analytical and computational models for describing and predicting material and structural performance.     

A pictorial index mechanism for model-based matching

We are currently developing unified query processing strategies for image databases. To perform this task, model-based representations of images by content are being used, as well as a hierarchical generalization of a relatively new object-recognition technique called data-driven indexed hypotheses. As the name implies, it is index-based, from which its efficiency derives. Earlier approaches to data-driven model-based object recognition techniques were not capable of handling complex image data containing overlapping, partially visible, and touching objects due to the limitations of the features used for building models. Recently, a few data-driven techniques capable of handling complex image data have been proposed. In these techniques, as in traditional databases, iconic index structures are employed to store the image and shape representation in such a way that searching for a given shape or image feature can be conducted efficiently. Some of these techniques handle the insertion and deletion of shapes and/or image representations very efficiently and with very little influence on the overall system performance. However, the main disadvantage of all previous data-driven implementations is that they are main memory based. In the present paper, we describe a secondary memory implementation of data-driven indexed hypotheses along with some performance studies we have conducted.     

A data-driven fault-tolerant control design of linear multivariable systems with performance optimization

In this paper, an integrated data-driven fault-tolerant control (FTC) design scheme is proposed under the configuration of the Youla parameterization for multiple-input multiple-output (MIMO) systems. With unknown system model parameters, the canonical form identification technique is first applied to design the residual observer in fault-free case. In faulty case, with online tuning of the Youla parameters based on the system data via the gradient-based algorithm, the fault influence is attenuated with system performance optimization. In addition, to improve the robustness of the residual generator to a class of system deviations, a novel adaptive scheme is proposed for the residual generator to prevent its over-activation. Simulation results of a two-tank flow system demonstrate the optimized performance and effect of the proposed FTC scheme.     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

Progress in Web-based decision support technologies

World Wide Web technologies have transformed the design, development, implementation and deployment of decision support systems. This article reviews and summarizes recent technology developments, current usage of Web-based DSS, and trends in the deployment of such systems. Many firms use the Web as a medium to convey information about DSS products or to distribute DSS software. The use of Web-based computation to provide product demonstrations or to deploy DSS applications for remote access remains less common. The academic literature on Web-based DSS is largely focused on applications and implementations, and only a few articles examine architectural issues or provide design guidelines based on empirical evidence.