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ABSTRACT: This article is devoted to iterative learning control (ILC) systems design for multiple-input multiple-output (MIMO), linear time-invariant (LTI) plants. With the bounded real lemma (BRL) applied, a linear matrix inequality (LMI) design approach is presented to develop sufficient conditions for the monotonic convergence of the ILC process. It is shown that regardless of a system relative degree, the convergence conditions can be expressed in terms of LMIs, and formulas can be derived for the learning gain matrices design. For ILC determined in this way, two illustrative examples are provided to verify its effectiveness and robustness against structured and polytopic-type uncertainties.
International Journal of Systems Science 11/2012; 43(11):2062-2071. · 0.99 Impact Factor
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ABSTRACT: This paper deals with the iterative learning control (ILC) design for multiple-input multiple-output (MIMO), time-delay systems
(TDS). Two feedback ILC schemes are considered using the so-called two-dimensional (2D) analysis approach. It shows that continuous-discrete
2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes, based on which necessary
and sufficient conditions for their stability can be provided. A numerical example is included to validate the theoretical
analysis.
KeywordsIterative learning control-Time-delay systems-2D analysis approach
Journal of Control Theory and Applications 04/2012; 8(4):457-463.
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ABSTRACT: Iterative learning control (ILC) is a kind of effective data-driven method that is developed based on online and/or offline input/output data. The main purpose of this paper is to supply a unified 2-D analysis approach for both continuous-time and discrete-time ILC systems with relative degree. It is shown that the 2-D Roesser system framework can be established for general ILC systems regardless of relative degree, under which convergence conditions can be provided to guarantee both asymptotic stability and monotonic convergence of the ILC processes. In particular, conditions for the monotonic convergence of ILC can be given in terms of linear matrix inequalities, and formulas for the updating law can be derived simultaneously. Simulation results are presented to illustrate the effectiveness of ILC determined through the 2-D design approach in dealing with the higher order relative degree problem of ILC systems, as well as the robustness of such ILC against uncertainties.
IEEE Transactions on Neural Networks 11/2011; 22(12):2213-25. · 2.95 Impact Factor
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ABSTRACT: This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD filters, our aim is to approximate the dynamics of the linear Gaussian jump Markov system (LGJMS) by a best-fitting Gaussian (BFG) distribution so that the GM-PHD smoother can be carried out with respect to an approximated linear Gaussian system. Our approach is inspired by the recognition that the BFG approximation provides an accurate performance measure for the LGJMS. Furthermore, the multiple model estimation is avoided and less computational cost is required. The effectiveness of the proposed smoother is verified with a numerical simulation.
American Control Conference (ACC), 2011; 08/2011
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ABSTRACT: This paper is devoted to designing iterative learning control (ILC) for multiple-input multiple-output discrete-time systems that are subject to random disturbances varying from iteration to iteration. Using the super-vector approach to ILC, statistical expressions are presented for both expectation and variance of the tracking error, and time-domain conditions are developed to ensure their asymptotic stability and monotonic convergence. It shows that time-domain conditions can be tied together with an H∞-based condition in the frequency domain by considering the properties of block Toeplitz matrices. This makes it possible to apply the linear matrix inequality technique to describe the convergence conditions and to obtain formulas for the control law design. Furthermore, the H∞-based approach is shown applicable to ILC design regardless of the system relative degree, which can also be used to address issues of model uncertainty. For a class of systems with a relative degree of one, simulation tests are provided to illustrate the effectiveness of the H∞-based approach to robust ILC design. Copyright © 2011 John Wiley & Sons, Ltd.
International Journal of Adaptive Control and Signal Processing 01/2011; 25(7):653 - 670. · 0.91 Impact Factor
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ABSTRACT: This paper deals with the stability analysis of discrete iterative learning control (ILC) by developing a two-dimensional (2-D) approach under the Roesser systems framework. The system under consideration is a class of multiple-input multiple-output (MIMO), linear time-varying (LTV) systems. Using a type of two-gain ILC, it is shown that once the theory is established for the discrete-time-varying 2-D Roesser systems, a necessary and sufficient condition for the stability of ILC process can be determined directly. Numerical simulation is included to verify the theoretical results.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
Asian Journal of Control 10/2010; 13(1):25 - 37. · 1.03 Impact Factor
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ABSTRACT: This paper introduces the key technologies and tourism applications of networking autonomic system. The paper focuses especially in the theories, architectures and algorithms being used. It discusses the requirements for networking autonomic system in China and introduces a data mining and intelligent predicting system of tourism emergency based on networking autonomic system, which concentrates on the methods, such as quantum immune clone, multi-level and multi-scale prediction model and local/global coordination mechanism of agent, in tourism data mining process.
Networking, Sensing and Control (ICNSC), 2010 International Conference on; 05/2010
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ABSTRACT: This paper is concerned with the iterative learning control (ILC) problem for discrete-time systems with iteration-varying disturbances. Using the so-called super-vector approach to ILC, the discrete domain bounded real lemma is employed to develop a sufficient condition ensuring both the stability and the desired H<sub>∞</sub> performance of the ILC process. It is shown that this sufficient condition can be presented in terms of linear matrix inequalities (LMIs), which can also determine the learning gain matrix. A numerical simulation example is included to validate the theoretical results.
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on; 01/2010
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ABSTRACT: This paper is devoted to the problem of H<sub>∞/sub> filtering for a class of neutral stochastic systems with both discrete and distributed time-varying delays. The objective is to design a full order filter such that the resulting filtering error system is stochastically asymptotically stable with a prescribed H<sub>∞</sub> performance satisfied. Based on the stability theory of stochastic systems, a delay-dependent and rate-dependent sufficient condition for the existence of filter is obtained in terms of linear matrix inequalities (LMIs). The corresponding filter design method is also proposed, while the explicit expression for the desired filter is given. A numerical example is finally included to illustrate the effectiveness of the proposed method.
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on; 01/2010
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ABSTRACT: This paper deals with the robust iterative learning control (ILC) for time-delay systems (TDS) with both model and delay uncertainties. An ILC algorithm with anticipation in time is considered, and a frequency-domain approach to its design is presented. It shows that a necessary and sufficient convergence condition can be provided in terms of three design parameters: the lead time, the learning gain, and the performance weighting function. In particular, if the lead time is chosen as just the delay estimate, then the convergence condition is derived independent of the delay and the uncertainties. In this case, with the selection of the performance weighting function, the perfect tracking can be achieved, or the least upper bound of the ¿<sub>2</sub>-norm of the limit tracking error can be guaranteed less than the least upper bound of the ¿<sub>2</sub>-norm of the initial tracking error.
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on; 01/2010
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Proceedings of the 49th IEEE Conference on Decision and Control, CDC 2010, December 15-17, 2010, Atlanta, Georgia, USA; 01/2010
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Proceedings of the 49th IEEE Conference on Decision and Control, CDC 2010, December 15-17, 2010, Atlanta, Georgia, USA; 01/2010
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Proceedings of the 49th IEEE Conference on Decision and Control, CDC 2010, December 15-17, 2010, Atlanta, Georgia, USA; 01/2010
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Int. J. Systems Science. 01/2010; 41:825-838.
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Proceedings of the IEEE International Conference on Networking, Sensing and Control, ICNSC 2010, Chicago, IL, USA, 10-12 April 2010; 01/2010
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ABSTRACT: This paper presents the system architecture, key technology and system realization for the travel route intelligent navigation system based on WEBGIS. Combining with Oracle spatial database and MapXtreme technology and using comprehensively position service, GIS and data mining and according to the traveler's type, preference, characteristics and requirement, the system can provide user information management, intelligent information processing and travel route planning services intelligently and personally.
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on; 12/2009
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ABSTRACT: This note is concerned with the robust discrete-time iterative learning control (ILC) design for nonlinear systems with varying initial state shifts. A two-gain ILC law is considered using a 2D analysis approach. Sufficient conditions are derived to guarantee both convergence of the learning process for fixed initial condition and boundedness of the tracking error for variable initial condition. It is shown that the error data with anticipation in time can well handle the varying initial state shifts in discrete-time ILC.
IEEE Transactions on Automatic Control 12/2009; · 2.11 Impact Factor
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ABSTRACT: This paper is mainly devoted to the iterative learning control (ILC) design for time-delay systems (TDS) in the presence of initial shifts, especially when the system parameters are subject to polytopic-type uncertainties. The ILC laws using a pure error term and/or an initial rectifying action to address the initial shifts are considered, and the two-dimensional (2-D) system theory is employed to develop necessary and sufficient conditions for the asymptotic stability of ILC. For the monotonic convergence of ILC, sufficient conditions are presented in terms of linear matrix inequalities (LMIs) based on the bounded real lemma (BRL). It is shown that adding the pure error term in the D-type learning law helps to meet certain LMIs to achieve a monotonically convergent ILC law. Specifically, this property is first investigated for linear time-invariant systems (LTIS), which is then discussed for the possible extension to TDS. Two numerical examples are included to illustrate the main results.
Circuits and Systems I: Regular Papers, IEEE Transactions on 09/2009; · 1.97 Impact Factor
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ABSTRACT: In this paper, a novel interacting multiple model (IMM) algorithm is proposed, which utilizes a multi-sensor optimal information fusion rule to combine multiple models in the linear minimum variance sense instead of famous Bayes' rule. Furthermore, the diagonal matrices are used as the updated weights of models, which are applied to distinguish the effects produced by different dimensions of state, so the new algorithm is named as diagonal interacting multiple model (DIMM) algorithm. Extensive Monte Carlo simulations indicate that the proposed DIMM algorithm has better accuracy of estimation than the IMM algorithm with no increase in the execution time, which confirm that the DIMM algorithm is a competitive alternative to the classical IMM algorithm.
American Control Conference, 2009. ACC '09.; 07/2009
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ABSTRACT: This paper investigates average consensus problem in networks of continuous-time agents with delayed information and jointly-connected topologies. A sufficient condition in terms of linear matrix inequalities (LMIs) is given under which all agents asymptotically reach average consensus, where the communication structures vary over time and the corresponding graphs may not be connected. Finally, simulation results are provided to demonstrate the effectiveness of our theoretical results.
American Control Conference, 2009. ACC '09.; 07/2009
