Joao Hespanha | University of California, Santa Barbara (original) (raw)
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We solve a linear quadratic optimal control problem for sampled-data systems with stochastic dela... more We solve a linear quadratic optimal control problem for sampled-data systems with stochastic delays. The delays are stochastically determined by the last few delays. The proposed optimal controller can be efficiently computed by iteratively solving a Riccati difference equation, provided that a discrete-time Markov jump system equivalent to the sampled-data system is stochastic stabilizable and detectable. Sufficient conditions for these notions are provided in the form of linear matrix inequalities, from which stabilizing controllers and state observers can be constructed.
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2018 Information Theory and Applications Workshop (ITA), 2018
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2019 IEEE 58th Conference on Decision and Control (CDC), 2019
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Asian Journal of Control, 2019
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European Journal of Control, 2018
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Noncooperative Game Theory, 2017
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2017 IEEE 56th Annual Conference on Decision and Control (CDC)
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arXiv (Cornell University), May 16, 2022
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2017 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS), 2017
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2020 American Control Conference (ACC), 2020
In recent years diverse computational models of emotional learning observed in the mammalian brai... more In recent years diverse computational models of emotional learning observed in the mammalian brain have inspired a number of self-learning control approaches. These architectures are promising in terms of their learning ability and low computational cost. However, the lack of rigorous stability analysis and mathematical proofs of stability and performance has limited the proliferation of these controllers. To address this drawback, this paper proposes a modified brain emotional neural network structure using a radial basis function inside the Thalamus and an emotional signal based on an integral action structure to increase performance. Mathematical stability proofs are provided, together with numerical simulations, demonstrating the superior performance obtained with the new modifications proposed to the emoional learning-inspired control.
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2020 59th IEEE Conference on Decision and Control (CDC), 2020
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Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control, 2021
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2019 IEEE 58th Conference on Decision and Control (CDC), 2019
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IFAC-PapersOnLine, 2020
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Mathematics of Control, Signals, and Systems, 2020
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Journal of the Royal Society Interface, Aug 6, 2014
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Siam Journal on Control and Optimization, 2009
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Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), Jul 10, 2003
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We solve a linear quadratic optimal control problem for sampled-data systems with stochastic dela... more We solve a linear quadratic optimal control problem for sampled-data systems with stochastic delays. The delays are stochastically determined by the last few delays. The proposed optimal controller can be efficiently computed by iteratively solving a Riccati difference equation, provided that a discrete-time Markov jump system equivalent to the sampled-data system is stochastic stabilizable and detectable. Sufficient conditions for these notions are provided in the form of linear matrix inequalities, from which stabilizing controllers and state observers can be constructed.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
2018 Information Theory and Applications Workshop (ITA), 2018
Bookmarks Related papers MentionsView impact
2019 IEEE 58th Conference on Decision and Control (CDC), 2019
Bookmarks Related papers MentionsView impact
Asian Journal of Control, 2019
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European Journal of Control, 2018
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Noncooperative Game Theory, 2017
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2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Bookmarks Related papers MentionsView impact
arXiv (Cornell University), May 16, 2022
Bookmarks Related papers MentionsView impact
2017 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS), 2017
Bookmarks Related papers MentionsView impact
2020 American Control Conference (ACC), 2020
In recent years diverse computational models of emotional learning observed in the mammalian brai... more In recent years diverse computational models of emotional learning observed in the mammalian brain have inspired a number of self-learning control approaches. These architectures are promising in terms of their learning ability and low computational cost. However, the lack of rigorous stability analysis and mathematical proofs of stability and performance has limited the proliferation of these controllers. To address this drawback, this paper proposes a modified brain emotional neural network structure using a radial basis function inside the Thalamus and an emotional signal based on an integral action structure to increase performance. Mathematical stability proofs are provided, together with numerical simulations, demonstrating the superior performance obtained with the new modifications proposed to the emoional learning-inspired control.
Bookmarks Related papers MentionsView impact
2020 59th IEEE Conference on Decision and Control (CDC), 2020
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Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control, 2021
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2019 IEEE 58th Conference on Decision and Control (CDC), 2019
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IFAC-PapersOnLine, 2020
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Mathematics of Control, Signals, and Systems, 2020
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Journal of the Royal Society Interface, Aug 6, 2014
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Siam Journal on Control and Optimization, 2009
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Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), Jul 10, 2003
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