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Gao, Yulong
Publications (5 of 5) Show all publications
Dai, L., Xia, Y., Gao, Y. & Cannon, M. (2018). Distributed stochastic MPC for systems with parameter uncertainty and disturbances. International Journal of Robust and Nonlinear Control, 28(6), 2424-2441
Open this publication in new window or tab >>Distributed stochastic MPC for systems with parameter uncertainty and disturbances
2018 (English)In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 28, no 6, p. 2424-2441Article in journal (Refereed) Published
Abstract [en]

A distributed stochastic model predictive control algorithm is proposed for multiple linear subsystems with both parameter uncertainty and stochastic disturbances, which are coupled via probabilistic constraints. To handle the probabilistic constraints, the system dynamics is first decomposed into a nominal part and an uncertain part. The uncertain part is further divided into 2 parts: the first one is constrained to lie in probabilistic tubes that are calculated offline through the use of the probabilistic information on disturbances, whereas the second one is constrained to lie in polytopic tubes whose volumes are optimized online and whose facets' orientations are determined offline. By permitting a single subsystem to optimize at each time step, the probabilistic constraints are then reduced into a set of linear deterministic constraints, and the online optimization problem is transformed into a convex optimization problem that can be performed efficiently. Furthermore, compared to a centralized control scheme, the distributed stochastic model predictive control algorithm only requires message transmissions when a subsystem is optimized, thereby offering greater flexibility in communication. By designing a tailored invariant terminal set for each subsystem, the proposed algorithm can achieve recursive feasibility, which, in turn, ensures closed-loop stability of the entire system. A numerical example is given to illustrate the efficacy of the algorithm. Copyright 

Place, publisher, year, edition, pages
John Wiley and Sons Ltd, 2018
Keywords
distributed control, model predictive control (MPC), probabilistic constraints, stochastic systems, Closed loop control systems, Constrained optimization, Convex optimization, Distributed parameter control systems, Model predictive control, Optimization, Predictive control systems, Stochastic control systems, Closed loop stability, Convex optimization problems, Message transmissions, Parameter uncertainty, Probabilistic information, Stochastic disturbances, Stochastic models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-227366 (URN)10.1002/rnc.4024 (DOI)000427013400030 ()2-s2.0-85041090534 (Scopus ID)
Note

Export Date: 9 May 2018; Article; CODEN: IJRCE; Correspondence Address: Xia, Y.; School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of TechnologyChina; email: xia_yuanqing@bit.edu.cn; Funding details: 61720106010, NSFC, National Natural Science Foundation of China; Funding details: 61621063, NSFC, National Natural Science Foundation of China; Funding details: 4161001, Natural Science Foundation of Beijing Municipality; Funding details: 61603041, NSFC, National Natural Science Foundation of China; Funding text: National Natural Science Foundation of China, Grant/Award Number: 61603041; Beijing Natural Science Foundation, Grant/Award Number: 4161001; National Natural Science Foundation Projects of International Cooperation and Exchanges, Grant/Award Number: 61720106010; Foundation for Innovative Research Groups of the National Natural Science Foundation of China, Grant/Award Number: 61621063; Funding text: This work was supported by the National Natural Science Foundation of China under grant 61603041, the Beijing Natural Science Foundation under grant 4161001, the National Natural Science Foundation Projects of International Cooperation and Exchanges under grant 61720106010, and by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under grant 61621063. QC 20180604

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-06-04Bibliographically approved
Gao, Y., Wu, S., Johansson, K. H., Shi, L. & Xie, L. (2018). Stochastic Optimal Control of Dynamic Queue Systems: A Probabilistic Perspective. In: 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV): . Paper presented at 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), NOV 18-21, 2018, Singapore, SINGAPORE (pp. 837-842). IEEE
Open this publication in new window or tab >>Stochastic Optimal Control of Dynamic Queue Systems: A Probabilistic Perspective
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2018 (English)In: 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), IEEE , 2018, p. 837-842Conference paper, Published paper (Refereed)
Abstract [en]

Queue overflow of a dynamic queue system gives rise to the information loss (or packet loss) in the communication buffer or the decrease of throughput in the transportation network. This paper investigates a stochastic optimal control problem for dynamic queue systems when imposing probability constraints on queue overflows. We reformulate this problem as a Markov decision process (MDP) with safety constraints. We prove that both finite-horizon and infinite-horizon stochastic optimal control for MDP with such constraints can be transformed as a linear program (LP), respectively. Feasibility conditions are provided for the finite-horizon constrained control problem. Two implementation algorithms are designed under the assumption that only the state (not the state distribution) can be observed at each time instant. Simulation results compare optimal cost and state distribution among different scenarios, and show the probability constraint satisfaction by the proposed algorithms.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Control Automation Robotics and Vision, ISSN 2474-2953
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-246317 (URN)10.1109/ICARCV.2018.8581152 (DOI)000459847700141 ()2-s2.0-85060814462 (Scopus ID)978-1-5386-9582-1 (ISBN)
Conference
15th International Conference on Control, Automation, Robotics and Vision (ICARCV), NOV 18-21, 2018, Singapore, SINGAPORE
Note

QC 20190319

Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-03-20Bibliographically approved
Dai, L., Gao, Y., Xie, L., Johansson, K. H. & Xia, Y. (2018). Stochastic self-triggered model predictive control for linear systems with probabilistic constraints. Automatica, 92, 9-17
Open this publication in new window or tab >>Stochastic self-triggered model predictive control for linear systems with probabilistic constraints
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2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 92, p. 9-17Article in journal (Refereed) Published
Abstract [en]

A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the self-triggered framework is that at each sampling instant, an optimization problem is solved to determine both the next sampling instant and the control inputs to be applied between the two sampling instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between sampling instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Model predictive control (MPC), Probabilistic constraints, Self-triggered control, Stochastic systems
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-227558 (URN)10.1016/j.automatica.2018.02.017 (DOI)000431159300002 ()2-s2.0-85045943840 (Scopus ID)
Funder
Swedish Research Council, 61633014Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20180509

Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-05-15Bibliographically approved
Gao, Y., Jafarian, M., Johansson, K. H. & Xie, L. (2017). Distributed Freeway Ramp Metering: Optimization on Flow Speed. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017: . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia. IEEE
Open this publication in new window or tab >>Distributed Freeway Ramp Metering: Optimization on Flow Speed
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies the distributed freeway ramp metering problem, for which the cell transmission model (CTM) is utilized. Considering the jam density and the upper bounds on the queue lengths and the ramp metering, we first provide feasibility conditions with respect to the external demand to ensure the controllability of the freeway. Assuming that the freeway is controllable, we formulate an optimization problem which tradeoffs the maximum average flow speed and the minimum waiting queue for each cell. Although the cells of the CTM are dynamically coupled, we propose a distributed backward algorithm for the optimization problem and prove that the solution to the problem is a Nash equilibrium. Furthermore, if the optimization problem is simplified to only maximization of the average flow speed, we argue that the obtained explicit distributed controller is globally optimal. A numerical example is given to illustrate the effectiveness of the proposed control algorithm.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223852 (URN)10.1109/CDC.2017.8264512 (DOI)000424696905070 ()2-s2.0-85046158057 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia
Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Research Council
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-06-01Bibliographically approved
Dai, L., Xia, Y., Gao, Y. & Cannon, M. (2017). Distributed Stochastic MPC of Linear Systems With Additive Uncertainty and Coupled Probabilistic Constraints. IEEE Transactions on Automatic Control, 62(7), 3474-3481
Open this publication in new window or tab >>Distributed Stochastic MPC of Linear Systems With Additive Uncertainty and Coupled Probabilistic Constraints
2017 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 7, p. 3474-3481Article in journal (Refereed) Published
Abstract [en]

This technical note develops a new form of distributed stochastic model predictive control (DSMPC) algorithm for a group of linear stochastic subsystems subject to additive uncertainty and coupled probabilistic constraints. We provide an appropriate way to design the DSMPC algorithm by extending a centralized SMPC (CSMPC) scheme. To achieve the satisfaction of coupled probabilistic constraints in a distributed manner, only one subsystem is permitted to optimize at each time step. In addition, by making explicit use of the probabilistic distribution of the uncertainties, probabilistic constraints are converted into a set of deterministic constraints for the predictions of nominal models. The distributed controller can achieve recursive feasibility and ensure closed-loop stability for any choice of update sequence. Numerical examples illustrate the efficacy of the algorithm.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Distributed control, model predictive control (MPC), probabilistic constraints, stochastic systems
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-210998 (URN)10.1109/TAC.2016.2612822 (DOI)000404299300033 ()2-s2.0-85028461202 (Scopus ID)
Note

QC 20170807

Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2018-09-19Bibliographically approved
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