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第28届中国控制与决策会议

作者： Yang Zhou Yuntao Shi College of Electrical and Control Engineering North China University of Technology The Key Lab of Field Bus and Automation of Beijing

ISBN: (纸本)9781467397155

This paper develops an approach for wind energy conversion system(WECS) control based on stochastic model predictive control(SMPC).The structure combines the Markov chain that illustrates the WECS behavior,a stochastic optimization method based on scenario,and quadratic *** using quadratic programming,SMPC can generally handle larger dimension of state model than stochastic dynamic programming for changes of WECS *** simulation results demonstrate the effectiveness of the optimal control of WECS by scenario-based stochastic model predictive control algorithm.

关键词： Wind Energy Conversion System Markov Chain Scenarios Tree stochastic model predictive control

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Traffic Engineering Based on stochastic model predictive control for Uncertain Traffic ChangeTraffic Engineering Based on stochastic model predictive control 14

Traffic Engineering Based on Stochastic Model Predictive Con...

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IFIP/IEEE International Symposium on Integrated Network Management (IM)

作者： Otoshi, Tatsuya Ohsita, Yuichi Murata, Masayuki Takahashi, Yousuke Ishibashi, Keisuke Shiomoto, Kohei Hashimoto, Tomoaki Osaka Univ Grad Sch Informat Sci & Technol 1-5 Yamadaoka Suita Osaka 5650871 Japan NTT Corp NTT Network Technol Labs Musashino Tokyo 1808585 Japan Osaka Univ Grad Sch Engn Sci Toyonaka Osaka 5608631 Japan

ISBN: (纸本)9783901882760

Traffic engineering (TE) plays an essential role in deciding routes that effectively use network resources. This is particularly important when one considers the increasing time variation of Internet traffic such as streaming and cloud services. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. This approach calculates routes from predicted traffic to avoid congestion, but predictions may include errors that instead cause congestion. We propose a prediction-based traffic engineering method that is robust to prediction errors by considering the probability distribution of predicted traffic. Our approach is based on a control-theoretic approach called stochastic model predictive control. Routes are calculated using a probability distribution of prediction errors so that the occurrence probability of congestion is lower than an operator-specified level. By considering the multi-step future dynamics of traffic, the routes are changed gradually to avoid route oscillation. We also show a relaxation method for unreliable far-future probabilistic constraints to avoid overly conservative route changes. Through simulations using backbone network traffic traces, we demonstrate that our method can accommodate most traffic variations under a given target link capacity without sudden large routes changes.

关键词： stochastic model predictive control Traffic Engineering Traffic Prediction Multi-path Routing

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stochastic model predictive control for constrained discrete-time Markovian switching systems

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AUTOMATICA 2014年第10期50卷 2504-2514页

作者： Patrinos, Panagiotis Sopasakis, Pantelis Sarimveis, Haralambos Bemporad, Alberto IMT Inst Adv Studies Lucca I-55100 Lucca Italy Natl Tech Univ Athens Sch Chem Engn Athens 15780 Greece

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MILS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MILS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control control of constrained systems stochastic switching systems

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Adaptively constrained stochastic model predictive control applied to security constrained optimal power flow

Adaptively constrained stochastic model predictive control a...

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American control Conference

作者： Oldewurtel, Frauke Roald, Line Andersson, Goeran Tomlin, Claire Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Swiss Fed Inst Technol Zurich ETHZ Dept Elect Engn Power Syst Lab Zurich Switzerland

ISBN: (纸本)9781479986842

The number of renewable energy sources in electricity grids is growing and with it the associated uncertainty. For power system operation a good tradeoff between security and cost becomes increasingly important. We consider the problem of including uncertainty from renewable in-feed in a DC security constrained optimal power flow (SCOPF) by formulating chance constraints. The chance-constrained SCOPF is solved repeatedly and can be understood as a chance-constrained model predictive control (MPC) problem, where the time coupling comes from storage in the system and the associated dynamics. The main focus of the paper is to reformulate the problem in a non-conservative way, i.e., to exploit the predefined violation level of the chance constraints based on their empirical closed-loop violations in order to achieve a good tradeoff between operational security and cost. We use and extend a recently proposed adaptive stochastic MPC scheme that starts with a standard conservative chance-constrained reformulation and then adapts the formulation of the constraints online based on the empirical violation level. The proposed approach is demonstrated on a 5-bus network.

关键词： stochastic model predictive control Chance constraints Adaptive control Closed-loop violation

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stochastic model predictive control for costs optimization in a supply chain under a stochastic demand 2

Stochastic Model Predictive Control for costs optimization i...

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2nd IEEE International Conference on Logistics Operations Management (GOL)

作者： Kawtar, Tikito Said, Achchab Youssef, Benadada Mohammed V Univ ENSIAS Rabat Morocco

ISBN: (纸本)9781479946501

This paper aims to present a stochastic model predictive control to optimize the costs of shipping and storage in a supply chain. The goal is to minimize the objective function of the combined costs in a multi-stage and a multi-level supply chain responding to a stochastic multi-product demand. The simulation using Matlab provides a comparison between classical models and the proposed model, and shows that the Affine Recourse stochastic model predictive control - AR SMPC offers better results.

关键词： Supply chain stochastic demand optimization of costs stochastic model predictive control

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Distributionally Robust Optimization Based model predictive control for stochastic Mixed Traffic Flow

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1491-1502页

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Tang, Yuliang Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Ind Intelligent Control & Ma Shanghai 200240 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China

In this paper, we investigate a mixed-traffic control problem considering uncertainties of HDVs flow. The challenges mainly lie in modeling the stochastic characteristics of mixed-traffic flow and developing less-conservative algorithm to deal with the uncertainties. To tackle the problem, we propose a stochastic model predictive control (MPC) strategy based on data-driven distributionally robust optimization (DRO). First, a stochastic mixed-traffic model, extended from cell transmission model, is proposed to describe the traffic dynamics. Then, utilizing historical traffic data, an incremental principal component analysis (IPCA) based method is given to construct ambiguity set and incorporate generalized moment information of uncertainties. Based on the above predictive model and ambiguity set, a DRO-based MPC problem is formulated and further converted into an equivalent dual form for efficient solutions, i.e., ramp metering and variable speed limit control. Finally, simulation results based on real data collected in Shanghai, China, demonstrate that our proposed strategy can significantly reduce traffic congestion, achieving 5.74% total travel time reduction compared to robust MPC.

关键词： Mixed-traffic flow ramp metering variable speed limit control data-driven distributionally robust optimization stochastic model predictive control

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control Lyapunov-Barrier function based model predictive control for stochastic nonlinear affine systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2024年第1期34卷 91-113页

作者： Zheng, Weijiang Zhu, Bing Beihang Univ Res Div 7 Beijing Peoples R China Beihang Univ Res Div 7 Beijing 100191 Peoples R China

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov-Barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi-linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled-data MPC framework to deal with states and inputs constraints, and to analyze stability of closed-loop systems. Moreover, event-triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example.

关键词： control Lyapunov-Barrier function dynamic feedback linearization event-triggering mechanisms sampled-data systems stochastic model predictive control

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Adaptively constrained stochastic model predictive control applied to security constrained optimal power flow

Adaptively constrained stochastic model predictive control a...

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American control Conference

作者： Frauke Oldewurtel Line Roald Goran Andersson Claire Tomlin Department of Electrical Engineering and Computer Science UC Berkeley USA Power Systems Laboratory Department of Electrical Engineering Swiss Federal Institute of Technology in Zurich (ETHZ) Switzerland

ISBN: (纸本)9781479917730

关键词： stochastic model predictive control Chance constraints Adaptive control Closed-loop violation optimal load flow adaptive control predictive control Security nonlinear model predictive control Power grids Renewable energy Cache Memory System stochastic models tradeoffs

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Decomposed stochastic model predictive control for Optimal Dispatch of Storage and Generation

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IEEE TRANSACTIONS ON SMART GRID 2014年第4期5卷 2044-2053页

作者： Zhu, Dinghuan Hug, Gabriela Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

This paper proposes a stochastic model predictive control (SMPC) approach to optimally dispatch energy storage and dispatchable generation in the electric energy system under uncertainties introduced by variable energy sources as well as demand. The objective is to minimize the expectation of the sum of the production and ramping costs for generators while satisfying all the system constraints. The uncertainties are represented by scenarios, resulting in a large-scale and computationally demanding optimization problem. We use the optimality condition decomposition (OCD) to decompose the SMPC problem into subproblems which can be solved in parallel thereby reducing the computation time. Both a scenario-based decomposition and a temporal-based decomposition are formulated and numerically evaluated in terms of speed and convergence using a modified IEEE 39-bus test system. Simulation results indicate that the scenario-based decomposition scheme achieves a better trade-off between convergence speed and subproblem size for the considered optimal dispatch problem.

关键词： Decomposition economic dispatch energy storage stochastic model predictive control variable renewable generation

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Risk in stochastic and Robust model predictive Path-Following control for Vehicular Motion Planning 34

Risk in Stochastic and Robust Model Predictive Path-Followin...

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34th IEEE Intelligent Vehicles Symposium (IV)

作者： Tolksdorf, Leon Tejada, Arturo van de Wouw, Nathan Birkner, Christian Eindhoven Univ Technol Dept Dynam & Control Eindhoven Netherlands Tech Hsch Ingolstadt CARISSMA Inst Safety Future Mobil Ingolstadt Germany TNO Integrated Vehicle Safety Helmond Netherlands

ISBN: (纸本)9798350346916

In automated driving, risk describes potential harm to passengers of an autonomous vehicle (AV) and other road users. Recent studies suggest that human-like driving behavior emerges from embedding risk in AV motion planning algorithms. Additionally, providing evidence that risk is minimized during the AV operation is essential to vehicle safety certification. However, there has yet to be a consensus on how to define and operationalize risk in motion planning or how to bound or minimize it during operation. In this paper, we define a stochastic risk measure and introduce it as a constraint into both robust and stochastic nonlinear model predictive path-following controllers (RMPC and SMPC respectively). We compare the vehicle's behavior arising from employing SMPC and RMPC with respect to safety and path-following performance. Further, the implementation of an automated driving example is provided, showcasing the effects of different risk tolerances and uncertainty growths in predictions of other road users for both cases. We find that the RMPC is significantly more conservative than the SMPC, while also displaying greater following errors towards references. Further, the RMPCs behavior cannot be considered as human-like. Moreover, unlike SMPC, the RMPC cannot account for different risk tolerances. The RMPC generates undesired driving behavior for even moderate uncertainties, which are handled better by the SMPC.

关键词： autonomous vehicles motion planning path following robust model predictive control stochastic model predictive control risk assessment

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