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Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory
2021 American Control Conference (ACC), 2021Providing stability guarantees for controllers that use neural networks can be challenging. Robust control theoretic tools are used to derive a framework for providing nominal stability guarantees – stability guarantees for a known nominal system – controlled by a learning-based neural network controller.
Rolf Findeisen +2 more
exaly +2 more sources
Chemistry of Materials, 2017
We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt +2 more
exaly +2 more sources
We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt +2 more
exaly +2 more sources
Experimentally verified Iterative Learning Control based on repetitive process stability theory
2012 American Control Conference (ACC), 2012This paper gives new results on the design and experimental evaluation of an Iterative Learning Control (ILC) law in a repetitive process setting. The experimental results given are from a gantry robot facility that has been extensively used in the benchmarking of linear model based ILC designs.
Pawel Grzegorz Dabkowski +7 more
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IFAC Proceedings Volumes, 2013
Abstract Iterative learning control has been developed for systems that repeat the same task over a finite duration with resetting to the starting location once each repetition, or trial, is complete. The novel feature is the use of information generated on the previous trial to compute the control input for the next one and the basic problem is to ...
Wojciech Paszke +2 more
openaire +1 more source
Abstract Iterative learning control has been developed for systems that repeat the same task over a finite duration with resetting to the starting location once each repetition, or trial, is complete. The novel feature is the use of information generated on the previous trial to compute the control input for the next one and the basic problem is to ...
Wojciech Paszke +2 more
openaire +1 more source
Analysis of robust control using stability theory of universal learning networks
IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003Nth order asymptotic orbital stability analysis method has been proposed to determine whether a nonlinear system is stable or not with large fluctuations of the system states. In this paper, we discuss the stability of robust control of a nonlinear crane system using this method.
null Yunqing Yu +3 more
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Advances in Computational Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sayan Mukherjee 0001 +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sayan Mukherjee 0001 +3 more
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Quantum Journal of Social Sciences and Humanities, 2022
The education model in the 21st century shall be learner-centered. Learners are expected to be independent to engage in self-directed learning with the integration of technological tools in developing necessary 21st century skills. However, the foundation of this education model shall not be neglected as positive emotion and motivation are the ...
openaire +1 more source
The education model in the 21st century shall be learner-centered. Learners are expected to be independent to engage in self-directed learning with the integration of technological tools in developing necessary 21st century skills. However, the foundation of this education model shall not be neglected as positive emotion and motivation are the ...
openaire +1 more source
2016 IEEE 55th Conference on Decision and Control (CDC), 2016
Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Eric Rogers +2 more
exaly +2 more sources
Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Eric Rogers +2 more
exaly +2 more sources
Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 2017
In this paper, a new learning algorithm based on Lyapunov stability theory for neural networks is used to improve the power system stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller.
Mohsen Farahani
exaly +2 more sources
In this paper, a new learning algorithm based on Lyapunov stability theory for neural networks is used to improve the power system stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller.
Mohsen Farahani
exaly +2 more sources