The Experts below are selected from a list of 18210 Experts worldwide ranked by ideXlab platform
J W Bandler - One of the best experts on this subject based on the ideXlab platform.
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space mapping optimization with adaptive surrogate model
IEEE Transactions on Microwave Theory and Techniques, 2007Co-Authors: Slawomir Koziel, J W BandlerAbstract:The proper choice of mapping used in space-mapping optimization algorithms is typically problem dependent. The number of parameters of the space-mapping surrogate model must be adjusted so that the model is flexible enough to reflect the features of the fine model, but at the same time is not over flexible. Its extrapolation capability should allow the prediction of the fine model response in the neighborhood of the Current Iteration point. A wrong choice of space-mapping type may lead to poor performance of the space-mapping optimization algorithm. In this paper, we consider a space-mapping optimization algorithm with an adaptive surrogate model. This allows us to adjust the type of space-mapping surrogate model used in a given Iteration based on the approximation/extrapolation capability of the model. The technique does not require any additional fine model evaluations
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a space mapping framework for engineering optimization theory and implementation
IEEE Transactions on Microwave Theory and Techniques, 2006Co-Authors: Slawomir Koziel, J W Bandler, Kaj MadsenAbstract:This paper presents a comprehensive approach to engineering design optimization exploiting space mapping (SM). The algorithms employ input SM and a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest. Output SM ensures the matching of responses and first-order derivatives between the mapped coarse model and the fine model at the Current Iteration point in the optimization process. We provide theoretical results that show the importance of the explicit use of sensitivity information to the convergence properties of our family of algorithms. Our algorithm is demonstrated on the optimization of a microstrip bandpass filter, a bandpass filter with double-coupled resonators, and a seven-section impedance transformer. We describe the novel user-oriented software package SMF that implements the new family of SM optimization algorithms
Slawomir Koziel - One of the best experts on this subject based on the ideXlab platform.
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space mapping optimization with adaptive surrogate model
IEEE Transactions on Microwave Theory and Techniques, 2007Co-Authors: Slawomir Koziel, J W BandlerAbstract:The proper choice of mapping used in space-mapping optimization algorithms is typically problem dependent. The number of parameters of the space-mapping surrogate model must be adjusted so that the model is flexible enough to reflect the features of the fine model, but at the same time is not over flexible. Its extrapolation capability should allow the prediction of the fine model response in the neighborhood of the Current Iteration point. A wrong choice of space-mapping type may lead to poor performance of the space-mapping optimization algorithm. In this paper, we consider a space-mapping optimization algorithm with an adaptive surrogate model. This allows us to adjust the type of space-mapping surrogate model used in a given Iteration based on the approximation/extrapolation capability of the model. The technique does not require any additional fine model evaluations
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a space mapping framework for engineering optimization theory and implementation
IEEE Transactions on Microwave Theory and Techniques, 2006Co-Authors: Slawomir Koziel, J W Bandler, Kaj MadsenAbstract:This paper presents a comprehensive approach to engineering design optimization exploiting space mapping (SM). The algorithms employ input SM and a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest. Output SM ensures the matching of responses and first-order derivatives between the mapped coarse model and the fine model at the Current Iteration point in the optimization process. We provide theoretical results that show the importance of the explicit use of sensitivity information to the convergence properties of our family of algorithms. Our algorithm is demonstrated on the optimization of a microstrip bandpass filter, a bandpass filter with double-coupled resonators, and a seven-section impedance transformer. We describe the novel user-oriented software package SMF that implements the new family of SM optimization algorithms
Chiangju Chien - One of the best experts on this subject based on the ideXlab platform.
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enhanced data driven optimal terminal ilc using Current Iteration control knowledge
IEEE Transactions on Neural Networks, 2015Co-Authors: Ronghu Chi, Zhongsheng Hou, Shangtai Jin, Danwei Wang, Chiangju ChienAbstract:In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain, which is a function about the system partial derivative with respect to the time-varying control input. In addition, a parameter updating law is designed to estimate the unknown partial derivatives iteratively. The input signals of the proposed E-DDOTILC are time-varying and updated utilizing not only the terminal tracking error of the previous run but also the input signals of the previous time instants in the Current Iteration. The proposed approach is a data-driven control strategy and only the I/O data are required for the controller design and analysis. The monotonic convergence and effectiveness of the proposed approach is further verified by both the rigorous mathematical analysis and the simulation results.
Yangquan Chen - One of the best experts on this subject based on the ideXlab platform.
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iterative learning control convergence robustness and applications
1999Co-Authors: Yangquan ChenAbstract:High-order iterative learning control of uncertain nonlinear systems with state delays.- High-order P-type iterative learning controller using Current Iteration tracking error.- Iterative learning control for uncertain nonlinear discrete-time systems using Current Iteration tracking error.- Iterative learning control for uncertain nonlinear discrete-time feedback systems with saturation.- Initial state learning method for iterative learning control of uncertain time-varying systems.- High-order terminal iterative learning control with an application to a rapid thermal process for chemical vapor deposition.- Designing iterative learning controllers via noncausal filtering.- Practical iterative learning control using weighted local symmetrical double-integral.- Iterative learning identification with an application to aerodynamic drag coefficient curve extraction problem.- Iterative learning control of functional neuromuscular stimulation systems.- Conclusions and future research.
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high order iterative learning control of discrete time nonlinear systems using Current Iteration tracking error
Iterative learning control, 1998Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:A P-type iterative learning controller (ILC) which includes a Current Iteration tracking error (CITE) in its high-order updating law is proposed for the tracking control of repetitive uncertain discrete-time nonlinear systems. It is shown that, under relaxed conditions, the tracking error bounds are class-K functions of the bounds of uncertainty, disturbance and the initialization error. The tracking error bound and the ILC convergence rate are tunable by the CITE learning gain. Moreover, the tracking error bound is shown to be a classK function of the bounds of the differences of initialization errors, uncertainties, and disturbances between two successive ILC repetitions. The effectiveness of the proposed ILC scheme is illustrated by simulation results of a single-link manipulator.
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a robust high order p type iterative learning controller using Current Iteration tracking error
International Journal of Control, 1997Co-Authors: Yangquan Chen, Changyun Wen, Mingxuan SunAbstract:A high-order P-type updating law, in which the Current Iteration tracking error (CITE) is employed, is proposed for iterative learning control (ILC ) of a class of uncertain nonlinear repetitive systems. Uniform boundedness of the tracking error is established in the presence of uncertainty, disturbance and initialization error. The bound and the ILC convergence rate can be adjusted by tuning the learning gain of the CITE.
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Current Iteration tracking error assisted iterative learning control of uncertain nonlinear discrete time systems
Conference on Decision and Control, 1996Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:A simple iterative learning controller (ILC) is proposed for the tracking control of uncertain discrete-time nonlinear systems performing the repetitive tasks. The tracking error of the Current learning Iteration is utilized in the ILC updating law. It is proven that, under relaxed conditions, the final tracking error is bounded in the presence of uncertainty, disturbance and the initialization error. Furthermore, the tracking error bound and the ILC convergence rate can be tuned by the learning gain of the Current Iteration tracking error in the ILC updating law. The effectiveness of the proposed ILC scheme is illustrated by a simulation.
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an iterative learning controller using Current Iteration tracking error information and initial state learning
Conference on Decision and Control, 1996Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:In this paper, an initial state learning scheme is proposed to remove the common assumption in the iterative learning control (ILC) that the initial states in each repetitive operation should be inside a given ball centered at the desired initial states, which may not be available. It is shown that the tracking error bound is independent of the initialization errors. By incorporating the Current tracking error information in the ILC updating law, the uniform bound of the tracking error as well as the ILC convergence rate can be adjusted to a desired level. A class of nonlinear time-varying uncertain systems are investigated. The effectiveness of the proposed iterative learning controller is illustrated by a simulation.
Tong Heng Lee - One of the best experts on this subject based on the ideXlab platform.
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high order iterative learning control of discrete time nonlinear systems using Current Iteration tracking error
Iterative learning control, 1998Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:A P-type iterative learning controller (ILC) which includes a Current Iteration tracking error (CITE) in its high-order updating law is proposed for the tracking control of repetitive uncertain discrete-time nonlinear systems. It is shown that, under relaxed conditions, the tracking error bounds are class-K functions of the bounds of uncertainty, disturbance and the initialization error. The tracking error bound and the ILC convergence rate are tunable by the CITE learning gain. Moreover, the tracking error bound is shown to be a classK function of the bounds of the differences of initialization errors, uncertainties, and disturbances between two successive ILC repetitions. The effectiveness of the proposed ILC scheme is illustrated by simulation results of a single-link manipulator.
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Current Iteration tracking error assisted iterative learning control of uncertain nonlinear discrete time systems
Conference on Decision and Control, 1996Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:A simple iterative learning controller (ILC) is proposed for the tracking control of uncertain discrete-time nonlinear systems performing the repetitive tasks. The tracking error of the Current learning Iteration is utilized in the ILC updating law. It is proven that, under relaxed conditions, the final tracking error is bounded in the presence of uncertainty, disturbance and the initialization error. Furthermore, the tracking error bound and the ILC convergence rate can be tuned by the learning gain of the Current Iteration tracking error in the ILC updating law. The effectiveness of the proposed ILC scheme is illustrated by a simulation.
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an iterative learning controller using Current Iteration tracking error information and initial state learning
Conference on Decision and Control, 1996Co-Authors: Yangquan Chen, Tong Heng LeeAbstract:In this paper, an initial state learning scheme is proposed to remove the common assumption in the iterative learning control (ILC) that the initial states in each repetitive operation should be inside a given ball centered at the desired initial states, which may not be available. It is shown that the tracking error bound is independent of the initialization errors. By incorporating the Current tracking error information in the ILC updating law, the uniform bound of the tracking error as well as the ILC convergence rate can be adjusted to a desired level. A class of nonlinear time-varying uncertain systems are investigated. The effectiveness of the proposed iterative learning controller is illustrated by a simulation.
