The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform
John Lygeros - One of the best experts on this subject based on the ideXlab platform.
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stochastic receding horizon control with bounded control inputs a vector space approach
IEEE Transactions on Automatic Control, 2011Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances while satisfying hard bounds on the control actions. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under marginal stability of the zero-control and zero-noise system we synthesize receding horizon polices that ensure bounded variance of the states while enforcing hard bounds on the controls. We provide examples that illustrate the effectiveness of our control strategies, and how quantities needed in the formulation of the resulting optimization problems can be calculated off-line.
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on stochastic receding horizon control with bounded control inputs
Conference on Decision and Control, 2009Co-Authors: Peter Hokayem, Debasish Chatterjee, John LygerosAbstract:This paper is concerned with the problem of receding horizon control of discrete-time systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control inputs. We use a nonlinear feedback policy with respect to noise measurements and show that the resulting mathematical program has a tractable Convex Solution. Moreover, under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state, under the resulting receding horizon control policy, is bounded. Finally, we provide some numerical examples on how certain matrices in the underlying mathematical program can be calculated off-line.
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stochastic model predictive control with bounded control inputs a vector space approach
arXiv: Optimization and Control, 2009Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances, while obeying hard bounds on the control inputs. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state is bounded when enforcing hard bounds on the control inputs, for any receding horizon implementation. Throughout the article we provide several examples that illustrate how quantities needed in the formulation of the resulting optimization problems can be calculated off-line, as well as comparative examples that illustrate the effectiveness of our control strategies.
Corentin Briat - One of the best experts on this subject based on the ideXlab platform.
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Convex conditions for robust stability analysis and stabilization of linear aperiodic impulsive and sampled data systems under dwell time constraints
Automatica, 2013Co-Authors: Corentin BriatAbstract:Stability analysis and control of linear impulsive systems is addressed in a hybrid framework, through the use of continuous-time time-varying discontinuous Lyapunov functions. Necessary and sufficient conditions for stability of impulsive systems with periodic impulses are first provided in order to set up the main ideas. Extensions to the stability of aperiodic systems under minimum, maximum and ranged dwell-times are then derived. By exploiting further the particular structure of the stability conditions, the results are non-conservatively extended to quadratic stability analysis of linear uncertain impulsive systems. These stability criteria are, in turn, losslessly extended to stabilization using a particular, yet broad enough, class of state-feedback controllers, providing then a Convex Solution to the open problem of robust dwell-time stabilization of impulsive systems using hybrid stability criteria. Relying finally on the representability of sampled-data systems as impulsive systems, the problems of robust stability analysis and robust stabilization of periodic and aperiodic uncertain sampled-data systems are straightforwardly solved using the same ideas. Several examples are discussed in order to show the effectiveness and reduced complexity of the proposed approach.
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Convex conditions for robust stability analysis and stabilization of linear aperiodic impulsive and sampled data systems under dwell time constraints
arXiv: Optimization and Control, 2013Co-Authors: Corentin BriatAbstract:Stability analysis and control of linear impulsive systems is addressed in a hybrid framework, through the use of continuous-time time-varying discontinuous Lyapunov functions. Necessary and sufficient conditions for stability of impulsive systems with periodic impulses are first provided in order to set up the main ideas. Extensions to stability of aperiodic systems under minimum, maximum and ranged dwell-times are then derived. By exploiting further the particular structure of the stability conditions, the results are non-conservatively extended to quadratic stability analysis of linear uncertain impulsive systems. These stability criteria are, in turn, losslessly extended to stabilization using a particular, yet broad enough, class of state-feedback controllers, providing then a Convex Solution to the open problem of robust dwell-time stabilization of impulsive systems using hybrid stability criteria. Relying finally on the representability of sampled-data systems as impulsive systems, the problems of robust stability analysis and robust stabilization of periodic and aperiodic uncertain sampled-data systems are straightforwardly solved using the same ideas. Several examples are discussed in order to show the effectiveness and reduced complexity of the proposed approach.
Ali Suleyman Ustunel - One of the best experts on this subject based on the ideXlab platform.
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Solution of the monge ampere equation on wiener space for general log concave measures
Journal of Functional Analysis, 2006Co-Authors: D Feyel, Ali Suleyman UstunelAbstract:Abstract In this work we prove that the unique 1-Convex Solution of the Monge–Kantorovitch measure transportation problem between the Wiener measure and a target measure which has an H-log-concave density, in the sense of Feyel and Ustunel [J. Funct. Anal. 176 (2000) 400–428], w.r.t the Wiener measure is also the strong Solution of the Monge–Ampere equation in the frame of infinite-dimensional Frechet spaces. We further enhance the polar factorization results of the mappings which transform a spread measure to another one in terms of the measure transportation of Monge–Kantorovitch and clarify the relation between this concept and the Ito-Solutions of the Monge–Ampere equation.
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Solution of the monge ampere equation on wiener space for log concave measures
arXiv: Probability, 2004Co-Authors: D Feyel, Ali Suleyman UstunelAbstract:In this work we prove that the unique 1-Convex Solution of the Monge problem contructed from the Solution of the Monge-Kantorovitch problem between the Wiener measure and a target measure which has a log-concave density w.r.to the Wiener measure is also the strong Solution of the Monge-Ampere equation in the frame of infinite dimensional Frechet spaces. We enhance also the polar factorization results of the mappings which transform a spread measure to another one of finite Wasserstein distance. Finally we calculate the semimartingale decomposition of the transport process with respect to its natural filtration and make the connection between the curved Brownian motion and the polar decomposition of the corresponding shifts.
Debasish Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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stochastic receding horizon control with bounded control inputs a vector space approach
IEEE Transactions on Automatic Control, 2011Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances while satisfying hard bounds on the control actions. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under marginal stability of the zero-control and zero-noise system we synthesize receding horizon polices that ensure bounded variance of the states while enforcing hard bounds on the controls. We provide examples that illustrate the effectiveness of our control strategies, and how quantities needed in the formulation of the resulting optimization problems can be calculated off-line.
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on stochastic receding horizon control with bounded control inputs
Conference on Decision and Control, 2009Co-Authors: Peter Hokayem, Debasish Chatterjee, John LygerosAbstract:This paper is concerned with the problem of receding horizon control of discrete-time systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control inputs. We use a nonlinear feedback policy with respect to noise measurements and show that the resulting mathematical program has a tractable Convex Solution. Moreover, under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state, under the resulting receding horizon control policy, is bounded. Finally, we provide some numerical examples on how certain matrices in the underlying mathematical program can be calculated off-line.
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stochastic model predictive control with bounded control inputs a vector space approach
arXiv: Optimization and Control, 2009Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances, while obeying hard bounds on the control inputs. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state is bounded when enforcing hard bounds on the control inputs, for any receding horizon implementation. Throughout the article we provide several examples that illustrate how quantities needed in the formulation of the resulting optimization problems can be calculated off-line, as well as comparative examples that illustrate the effectiveness of our control strategies.
Peter Hokayem - One of the best experts on this subject based on the ideXlab platform.
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stochastic receding horizon control with bounded control inputs a vector space approach
IEEE Transactions on Automatic Control, 2011Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances while satisfying hard bounds on the control actions. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under marginal stability of the zero-control and zero-noise system we synthesize receding horizon polices that ensure bounded variance of the states while enforcing hard bounds on the controls. We provide examples that illustrate the effectiveness of our control strategies, and how quantities needed in the formulation of the resulting optimization problems can be calculated off-line.
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on stochastic receding horizon control with bounded control inputs
Conference on Decision and Control, 2009Co-Authors: Peter Hokayem, Debasish Chatterjee, John LygerosAbstract:This paper is concerned with the problem of receding horizon control of discrete-time systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control inputs. We use a nonlinear feedback policy with respect to noise measurements and show that the resulting mathematical program has a tractable Convex Solution. Moreover, under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state, under the resulting receding horizon control policy, is bounded. Finally, we provide some numerical examples on how certain matrices in the underlying mathematical program can be calculated off-line.
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stochastic model predictive control with bounded control inputs a vector space approach
arXiv: Optimization and Control, 2009Co-Authors: Debasish Chatterjee, Peter Hokayem, John LygerosAbstract:We design receding horizon control strategies for stochastic discrete-time linear systems with additive (possibly) unbounded disturbances, while obeying hard bounds on the control inputs. We pose the problem of selecting an appropriate optimal controller on vector spaces of functions and show that the resulting optimization problem has a tractable Convex Solution. Under the assumption that the zero-input and zero-noise system is asymptotically stable, we show that the variance of the state is bounded when enforcing hard bounds on the control inputs, for any receding horizon implementation. Throughout the article we provide several examples that illustrate how quantities needed in the formulation of the resulting optimization problems can be calculated off-line, as well as comparative examples that illustrate the effectiveness of our control strategies.