The Experts below are selected from a list of 16875 Experts worldwide ranked by ideXlab platform
R.h. Middleton - One of the best experts on this subject based on the ideXlab platform.
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Dealing with Time-Varying Parameter problem of robot manipulators performing path tracking tasks
IEEE Transactions on Automatic Control, 1992Co-Authors: Y.d. Song, R.h. MiddletonAbstract:Using the properties of the element by element (or Hadamard) product of matrices, the authors obtain the robot dynamics in Parameter-isolated form, from which a new control scheme is developed. The controller proposed yields zero asymptotic tracking errors when applied to robotic systems with Time-Varying Parameters by using a switching type control law. The results obtained are global in the initial state of the robot, and can be applied to rapidly varying systems.
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Dealing with the Time-Varying Parameter problem of robot manipulators performing path tracking tasks
29th IEEE Conference on Decision and Control, 1990Co-Authors: Y.d. Song, R.h. MiddletonAbstract:The path tracking problem of a robot manipulator with Time-Varying Parameters is considered. Using the properties of the element-by-element product of matrices, the robot dynamics is obtained in Parameter-isolated form, from which a new control scheme is developed. The stability is addressed by verifying an integral inequality motivated by the Barbalat lemma, rather than the more common Lyapunov method. The proposed controller yields zero asymptotic tracking errors when applied to robotic systems with Time-Varying Parameters by using a switching-type control law. It is shown that the controller presented does not involve much online computation since it only depends on bounds on the variation of the system Parameters, which can easily be predetermined offline.
Y.d. Song - One of the best experts on this subject based on the ideXlab platform.
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Dealing with Time-Varying Parameter problem of robot manipulators performing path tracking tasks
IEEE Transactions on Automatic Control, 1992Co-Authors: Y.d. Song, R.h. MiddletonAbstract:Using the properties of the element by element (or Hadamard) product of matrices, the authors obtain the robot dynamics in Parameter-isolated form, from which a new control scheme is developed. The controller proposed yields zero asymptotic tracking errors when applied to robotic systems with Time-Varying Parameters by using a switching type control law. The results obtained are global in the initial state of the robot, and can be applied to rapidly varying systems.
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Dealing with the Time-Varying Parameter problem of robot manipulators performing path tracking tasks
29th IEEE Conference on Decision and Control, 1990Co-Authors: Y.d. Song, R.h. MiddletonAbstract:The path tracking problem of a robot manipulator with Time-Varying Parameters is considered. Using the properties of the element-by-element product of matrices, the robot dynamics is obtained in Parameter-isolated form, from which a new control scheme is developed. The stability is addressed by verifying an integral inequality motivated by the Barbalat lemma, rather than the more common Lyapunov method. The proposed controller yields zero asymptotic tracking errors when applied to robotic systems with Time-Varying Parameters by using a switching-type control law. It is shown that the controller presented does not involve much online computation since it only depends on bounds on the variation of the system Parameters, which can easily be predetermined offline.
Dennis S. Bernstein - One of the best experts on this subject based on the ideXlab platform.
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Kalman-filter-based Time-Varying Parameter estimation via retrospective optimization of the process noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the process noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to Parameter estimation problems, where the Parameters to be estimated are Time-Varying and thus do not fit in the classical Kalman filter noise structure. This technique is compared to the standard Kalman filter with a fixed process noise covariance as well as an innovations-based adaptive Kalman filter.
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ACC - Kalman-filter-based Time-Varying Parameter estimation via retrospective optimization of the process noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the process noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to Parameter estimation problems, where the Parameters to be estimated are Time-Varying and thus do not fit in the classical Kalman filter noise structure. This technique is compared to the standard Kalman filter with a fixed process noise covariance as well as an innovations-based adaptive Kalman filter.
Mahdi Khodayar - One of the best experts on this subject based on the ideXlab platform.
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Deep Learning-Based Time-Varying Parameter Identification for System-Wide Load Modeling
IEEE Transactions on Smart Grid, 2019Co-Authors: Xinan Wang, Chen Chen, Ying Zhang, Mahdi KhodayarAbstract:The integration of uncertain power resources is causing more challenges for traditional load modeling research. Parameter identification of load modeling is impacted by a variety of load components with Time-Varying characteristics. This paper develops a deep learning-based Time-Varying Parameter identification model for composite load modeling (CLM) with ZIP load and induction motor. A multi-modal long short-term memory (M-LSTM) deep learning method is used to estimate all the Time-Varying Parameters of CLM considering system-wide measurements. It contains a multi-modal structure that makes use of different modalities of the input data to accurately estimate Time-Varying load Parameters. An LSTM network with a flexible number of temporal states is defined to capture powerful temporal patterns from the load Parameters and measurements time series. The extracted features are further fed to a shared representation layer to capture the joint representation of input time series data. This temporal representation is used in a linear regression model to estimate Time-Varying load Parameters at the current time. Numerical simulations on the 23- and 68-bus systems verify the effectiveness and robustness of the proposed M-LSTM method. Also, the optimal lag values of Parameters and measurements as input variables are solved.
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Probabilistic Time-Varying Parameter Identification for Load Modeling: A Deep Generative Approach
IEEE Transactions on Industrial Informatics, 1Co-Authors: Mahdi Khodayar, Jianhui WangAbstract:The uncertainty of power resources introduces significant challenges for classic load modeling approaches. Moreover, load Parameter identification techniques are affected by various load components with highly nonlinear Time-Varying behaviors and dependencies. This paper presents a new deep generative architecture (DGA) based on the long-short term memory (LSTM) network for probabilistic Time-Varying Parameter identification (PTVPI). In contrast to previous methods that merely compute point estimations of load Parameters, our objective is to learn the continuous probability density function (PDF) of load Parameters for composite load modeling (CLM) with ZIP load and induction motor (IM). The proposed DGA learns complex temporal patterns from the Time-Varying Parameters/measurements to estimate load Parameters in a probabilistic fashion. Leveraging the LSTM network, our DGA computes deep temporal states and state transitions of load Parameters. An encoding neural network extracts useful latent variables from the captured temporal states that are further mapped by a decoding neural network into the observed load Parameters; hence, learning the underlying PDF of these Parameters. Numerical results on the 68-bus New England and New York Interconnect System with four CLMs show accurate results for PTVPI in terms of various probabilistic estimation metrics including reliability, sharpness, and continuous ranked probability score.
Frantisek M. Sobolic - One of the best experts on this subject based on the ideXlab platform.
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Kalman-filter-based Time-Varying Parameter estimation via retrospective optimization of the process noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the process noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to Parameter estimation problems, where the Parameters to be estimated are Time-Varying and thus do not fit in the classical Kalman filter noise structure. This technique is compared to the standard Kalman filter with a fixed process noise covariance as well as an innovations-based adaptive Kalman filter.
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ACC - Kalman-filter-based Time-Varying Parameter estimation via retrospective optimization of the process noise covariance
2016 American Control Conference (ACC), 2016Co-Authors: Frantisek M. Sobolic, Dennis S. BernsteinAbstract:Retrospective estimation of the process noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to Parameter estimation problems, where the Parameters to be estimated are Time-Varying and thus do not fit in the classical Kalman filter noise structure. This technique is compared to the standard Kalman filter with a fixed process noise covariance as well as an innovations-based adaptive Kalman filter.
