Qing-Guo Wang

National University of Singapore, Tumasik, Singapore

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Publications (199)353.44 Total impact

  • Dan Zhang, Rongyao Ling, Qing-Guo Wang, Li Yu, Yu Feng
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    ABSTRACT: This paper is concerned with the sensor-network-based distributed stabilization of nonlinear large-scale systems with energy constraints and random sensor faults. Due to the limited power in sensors, techniques such as reduction of times and size of the transmission packet are utilized to save the energy. As for the sensor failure phenomenon, a set of binary variables is introduced to model it. Based on the switched system theory, the Lyapunov stability technique and some stochastic system analysis, a sufficient condition is established under which the closed-loop system is exponentially stable in the mean-square sense and achieves a prescribed disturbance attenuation level. The controller gain design algorithm is presented by using the cone complementarity linearization (CCL) method. A numerical example is finally given to show the effectiveness of the proposed design.
    Journal of the Franklin Institute 02/2015; DOI:10.1016/j.jfranklin.2015.01.028 · 2.26 Impact Factor
  • Binh Nguyen Le, Qing-Guo Wang, Tong Heng Lee
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    ABSTRACT: This paper deals with the problem of determining the stabilizing controller gain and plant delay ranges for a general delay system in feedback configuration. Such a problem admits no analytical solutions in general. Instead, the condition of the loop Nyquist plot's intersection with the critical point is employed to graphically determine stability boundaries in the gain-delay space and stability of regions divided by these boundaries is decided with the help of a new perturbation analysis of delay on change of closed-loop unstable poles. As a result, all the stable regions are obtained and each stable region captures the full information on the stabilizing gain intervals versus any delay of the process. The proposed method is applicable to both stable and unstable processes of any order with or without the right-half plane zeros. Several examples are provided for illustration and comparison with the existing methods.
    Journal of Process Control 01/2015; 25. DOI:10.1016/j.jprocont.2013.12.019 · 2.18 Impact Factor
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    ABSTRACT: A graphical method is extended to determine the stabilizing gain and delay ranges for a bi-proper delay system.•A bi-proper process is rare but causes great complications for the method because of possibility of infinite intersections of boundary functions within a finite delay range.•The properties of boundary functions from such processes are investigated in great details to show that finite boundary functions are sufficient to determine all stable regions for finite parameter intervals.•The formula is given for calculating this number.•Algorithms are established to find exact stabilizing gain and delay ranges.
    ISA Transactions 10/2014; DOI:10.1016/j.isatra.2014.09.014 · 2.26 Impact Factor
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    ABSTRACT: This paper addresses the design of robust track-following dynamic output feedback controller for hard disk drives (HDDs) in face of parameter uncertainties which can enter into problem description in a possibly non-linear way. The design is performed in a probabilistic framework where the uncertain parameters are treated as random variables and the design specification is met with a given probability level. In particular, a sequential algorithm based on gradient iteration is employed to find a probabilistic robust feasible solution to the formulated problem. The design procedure is computationally tractable and its computational complexity does not depend on the number of uncertain parameters. Our case study allows natural frequency and damping ratio to vary within 8% and 10% from their nominal values for rigid body and all resonance modes. The designed controller achieves robustness in the presence of these uncertainties. Furthermore, the designed controller is implemented in real time on a commercial HDD.
    Mechatronics 09/2014; 24(6). DOI:10.1016/j.mechatronics.2014.02.007 · 1.82 Impact Factor
  • Dan Zhang, Wenjian Cai, Qing-Guo Wang
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    ABSTRACT: This paper is concerned with the mixed H∞H∞ and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set Γ={0,1}Γ={0,1} is introduced to model this phenomenon. Based on the Markovian system approach and linear matrix inequality technique, a new sufficient condition has been derived such that the estimation error system is exponentially stable in the mean square sense and achieves a prescribed mixed H∞H∞ and passivity performance level. The estimator parameter is then determined by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method.
    Neurocomputing 09/2014; 139:321–327. DOI:10.1016/j.neucom.2014.02.025 · 2.01 Impact Factor
  • Dan Zhang, Wenjian Cai, Qing-Guo Wang
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    ABSTRACT: This paper is concerned with the energy-efficient filtering for a class of wireless sensor networks (WSNs). Due to the power limitation of WSNs, the measurement signal is transmitted to the remote filter infrequently and stochastically. A stochastic framework is proposed to formulate the filtering problem for such systems. A sufficient condition is established such that the filtering error system is mean-square stable and achieves a prescribed disturbance attenuation level in the H∞H∞ sense. The optimal filter design is presented to determine the filter gains. Relations between the transmission parameters, e.g., transmission probability, transmission intervals and the filtering performance are obtained. Finally, a continuous stirred tank reactor (CSTR) system is employed to evaluate the effectiveness of the proposed design.
    Signal Processing 08/2014; 101:134–141. DOI:10.1016/j.sigpro.2014.01.032 · 2.24 Impact Factor
  • Dan Zhang, Wenjian Cai, Qing-Guo Wang
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    ABSTRACT: This paper is concerned with the robust non-fragile filtering for a class of networked systems with distributed variable delays. We model such a complex delay system with an augmented switched system. For the filtering implementation uncertainty, a stochastic variable is employed to indicate random occurrence of the filter gain change, and a norm bound to measure the change size. The suitably weighted measurements are proposed for filter performance improvement, instead of direct use of the measurements themselves which may have significant delays and degrade the performance. With some improved stability and l 2 gain analysis for the switched systems, a new sufficient condition is obtained such that the filtering error system is exponentially stable in the mean square sense and achieves a prescribed H∞H∞ performance level. A numerical example is given to show the effectiveness of the proposed design.
    Journal of the Franklin Institute 07/2014; 351(7). DOI:10.1016/j.jfranklin.2014.03.009 · 2.26 Impact Factor
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    ABSTRACT: This paper is concerned with the non-fragile H∞ filtering for a class of discrete-time networked systems with multiple communication delays. We model such a complex delay system as a switched system. For the filtering implementation uncertainty, a stochastic variable is employed to describe the phenomenon of the randomly occurring filter gain change, and a norm bound is used to measure the change size. With the switched system theory and the stochastic system analysis, a new sufficient condition is obtained such that the filtering error system is exponentially stable in the mean square sense and achieves a prescribed H∞ performance level. A numerical example is given to show the effectiveness of the proposed design.
    2014 11th IEEE International Conference on Control & Automation (ICCA); 06/2014
  • Qing-Guo Wang, Binh-Nguyen Le, Tong-Heng Lee
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    ABSTRACT: An effective method is presented to find PID stabilizing region in controller parameters plane. The concept of stability boundaries in D-decomposition technique is extended to the parameterized stability boundary, which transforms boundary curves into boundary bands when one of the controller gains varies in a range. This eliminates the difficulty of using 3D graph to solve the problem with 3 parameters while maintaining the advantage of 2D method.
    2014 11th IEEE International Conference on Control & Automation (ICCA); 06/2014
  • Qing-Guo Wang, Chao Yu, Yong Zhang
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    ABSTRACT: This paper proposes an improved system identification method with Renormalization Group. Renormalization Group is applied to a fine data set to obtain a coarse data set. The least squares algorithm is performed on the coarse data set. The theoretical analysis under certain conditions shows that the parameter estimation error could be reduced. The proposed method is illustrated with examples.
    ISA Transactions 01/2014; 53(5). DOI:10.1016/j.isatra.2013.10.003 · 2.26 Impact Factor
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    ABSTRACT: In this paper, we consider uncertain linear and bilinear matrix inequalities which depend in a possibly nonlinear way on a vector of uncertain parameters. Motivated by recent results in statistical learning, we show that probabilistic guaranteed solutions can be obtained by means of randomized algorithms. In particular, we show that the Vapnik-Chevonenkis dimension (VC-dimension) of the two problems is finite, and we compute upper bounds on it. In turn, these bounds allow us to derive explicitly the sample complexity of the problems. Using these bounds, in the second part of the paper, we derive a sequential scheme, based on a sequence of optimization and validation steps. The algorithm is on the same lines of recent schemes proposed for similar problems, but improves both in terms of complexity and generality.
    2013 IEEE 52nd Annual Conference on Decision and Control (CDC); 12/2013
  • Dan Zhang, Qing-Guo Wang, Li Yu, Haiyu Song
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    ABSTRACT: This paper is concerned with the fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements where there are significant uncertainties on information. A unified model is proposed to capture four sources of these uncertainties, namely, the sensor saturation, the signal quantization, the general medium access constraint, and the multiple packet dropouts. A simultaneous consideration of these issues reflects the practical networked systems much more closely than the existing works. The goal of this paper is to design a fault detector such that, for all unknown input, control input, and uncertain information, the estimation error between the residual and the fault is minimized. Using the switched system approach and some stochastic analyses, a sufficient condition for the existence of desired fault detector is established and the fault detector gains are computed by solving an optimization problem. Two numerical examples are given to show the effectiveness of the proposed design.
    IEEE Transactions on Instrumentation and Measurement 12/2013; 62(12):3148-3159. DOI:10.1109/TIM.2013.2272865 · 1.71 Impact Factor
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    Tao Liu, Qing-Guo Wang, Hsiao-Ping Huang
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    ABSTRACT: Step and relay feedback tests have been widely used for model identification in the process industry. The corresponding identification methods developed in the past three decades are surveyed in this paper. Firstly, the process models with time delay mainly adopted for identification in the literature are presented with a classification on different response types. By categorizing the major technical routes developed in the existing references for parameter estimation relating to different applications, the identification methods are subsequently clustered into groups for overview, along with two specific categories for robust identification against load disturbance and the identification of multivariable or nonlinear processes. The rationales of each category are briefly explained, while a typical or state-of-the-art identification algorithm of each category is elucidated along with application to benchmark examples from the literature for illustrating the achievable accuracy and robustness, so as to facilitate the readers to have a general knowledge of the research development. Finally, an outlook on the open issues regarding step or relay identification is provided to call attention to future exploration.
    Journal of Process Control 11/2013; 23(10):1597-1623. DOI:10.1016/j.jprocont.2013.08.003 · 2.18 Impact Factor
  • Dan Zhang, Li Yu, Hongbo Song, Qing-Guo Wang
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    ABSTRACT: In this article, the distributed H ∞ filtering problem is investigated for a class of sensor networks under topology switching. The main purpose is to design the distributed H ∞ filter that allows one to regulate the sensor's working modes. Firstly, a switched system model is proposed to reflect the working mode change of the sensors. Then, a stochastic sequence is adopted to model the packet dropout phenomenon occurring in the channels from the plant to the networked sensors. By utilising the Lyapunov functional method and stochastic analysis, some sufficient conditions are established to ensure that the filtering error system is mean-square exponentially stable with a prescribed H ∞ performance level. Furthermore, the filter parameters are determined by solving a set of linear matrix inequalities LMIs. Our results relates the decay rate of the filtering error system to the switching frequency of the topology directly and shows the existence of such a distributed filter when the topology is not varying very frequently, which is helpful for the sensor state regulation. Finally, the effectiveness of the proposed design method is demonstrated by two numerical examples.
    International Journal of Systems Science 11/2013; 44(11):2104-2118. DOI:10.1080/00207721.2012.684903 · 1.58 Impact Factor
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    ABSTRACT: The present paper addresses the design of discrete time robust ${\mathcal{H}}_2$ H 2 track following dynamic output feedback controller for hard disk drives where uncertain parameters enter in a non-linear fashion into plant description. Uncertain parameters are considered as random variables with uniform distribution. The controller is designed to meet the performance specification with desired probabilistic levels (accuracy and confidence). The design is benefited from convex optimization in design parameter space and randomization in the uncertainty space. A localization method based on analytic center cutting plane algorithm is employed in order to find the probabilistic robust feasible solution. As a result of randomization, the computational complexity of the algorithm does not depend on the number of uncertain parameters and no conservatism is introduced while handling uncertain parameters. The effectiveness of the designed controller is verified through simulation as well as experiment.
    Microsystem Technologies 09/2013; 19(9-10). DOI:10.1007/s00542-013-1827-7 · 0.95 Impact Factor
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    ABSTRACT: In this paper, we consider the problem of minimizing a linear functional subject to uncertain linear and bilinear matrix inequalities, which depend in a possibly nonlinear way on a vector of uncertain parameters. Motivated by recent results in statistical learning theory, we show that probabilistic guaranteed solutions can be obtained by means of randomized algorithms. In particular, we show that the Vapnik-Chervonenkis dimension (VC-dimension) of the two problems is finite, and we compute upper bounds on it. In turn, these bounds allow us to derive explicitly the sample complexity of these problems. Using these bounds, in the second part of the paper, we derive a sequential scheme, based on a sequence of optimization and validation steps. The algorithm is on the same lines of recent schemes proposed for similar problems, but improves both in terms of complexity and generality. The effectiveness of this approach is shown using a linear model of a robot manipulator subject to uncertain parameters.
    Automatica 05/2013; 50(6). DOI:10.1016/j.automatica.2014.04.005 · 3.13 Impact Factor
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    ABSTRACT: Motivated by the complexity of solving convex scenario problems in one-shot, in this paper we provide a direct connection between this approach and sequential randomized methods. A rigorous analysis of the theoretical properties of two new algorithms, for full constraint satisfaction and partial constraint satisfaction, is provided. These algorithms allow to enlarge the applicability of scenario-based methods to real-world applications involving a large number of design variables. Extensive numerical simulations for a non-trivial application regarding hard-disk drive servo design testify the goodness of the proposed solution.
  • Zhuo-Yun Nie, Min Wu, Qing-Guo Wang, Yong He
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    ABSTRACT: This paper addresses the problem of loop gain and phase margins of two-input two-output (TITO) systems. A new frequency domain approach is proposed to accurately compute the loop gain and phase margins for TITO systems. With the help of geometry analysis method, the stability boundaries are shown to be the intersection points of some constructed curves. The computational burden is reduced by restricting the frequency range estimated by the norm analysis. The gain and phase margins are determined in the stable region. The method is demonstrated by two examples.
    Journal of the Franklin Institute 04/2013; 350(3):503–520. DOI:10.1016/j.jfranklin.2012.12.011 · 2.26 Impact Factor
  • Qing-Guo Wang, Chao Yu, Yong Zhang
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    ABSTRACT: This paper proposes a new method for model assessment based on Renormalization Group. Renormalization Group is applied to the original data set to obtain the transformed data set with the majority rule to set its labels. The assessment is first performed on the data level without invoking any learning method, and the consistency and nonrandomness indices are defined by comparing two data sets to reveal informative content of the data. When the indices indicate informative data, the next assessment is carried out at the model level, and the predictions are compared between two models learnt from the original and transformed data sets, respectively. The model consistency and reliability indices are introduced accordingly. Unlike cross-validation and other standard methods in the literature, the proposed method creates a new data set and data assessment. Besides, it requires only two models and thus less computational burden for model assessment. The proposed method is illustrated with academic and practical examples.
    Control and Automation (ICCA), 2013 10th IEEE International Conference on; 01/2013
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    ABSTRACT: Motivated by the complexity of solving convex scenario problems in one-shot, two new algorithms for the sequential solution of sampled convex optimization problems are presented, for full constraint satisfaction and partial constraint satisfaction, respectively. A rigorous analysis of the theoretical properties of the algorithms is provided, and the related sample complexity is derived. Extensive numerical simulations for a non-trivial example testify the goodness of the proposed solution.
    Computer Aided Control System Design (CACSD), 2013 IEEE Conference on; 01/2013

Publication Stats

5k Citations
353.44 Total Impact Points

Institutions

  • 2007–2014
    • National University of Singapore
      • Department of Electrical & Computer Engineering
      Tumasik, Singapore
    • Central South University
      • School of Information Science and Engineering
      Changsha, Hunan, China
    • Qingdao University
      • Institute of Complexity Sciences
      Tsingtao, Shandong Sheng, China
  • 2004–2006
    • University of Houston
      • Department of Electrical & Computer Engineering
      Houston, Texas, United States