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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Csaji, Balazs Cs Kis, Krisztian B. Kovacs, Andras Inst Comp Sci & Control SZTAKI Kende Utca 13-17 H-1111 Budapest Hungary

The paper applies the scenario approach to stochastic model predictive control for renewable energy systems. First, the controllable and the (quasi-periodic) uncontrollable parts are decomposed. The latter is modeled by a Box-Jenkins system with appropriately chosen inputs. For the controllable part, a linear state space model is used with an affine state-feedback controller. Several numerical experiments are presented on a public lighting microgrid, e.g., about forecasting the energy balance, the effects of various controller parametrizations, reoptimization frequencies, and discarding unfavorable scenarios. The results indicate that even a low order, time-independent controller with a slow reoptimization frequency can be efficient. Copyright (C) 2020 The Authors.

关键词： stochastic model predictive control chance-constrained optimization partially controllable systems randomized methods scenario approach renewable energy systems

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Successive Linearization based stochastic model predictive control for batch processes described by DAEs 6

Successive Linearization based Stochastic Model Predictive C...

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6th Conference on Advances in control and Optimization of Dynamical Systems (ACODS)

作者： Atmaram, Laxman Leema Kodamanat, Hariprasad Indian Inst Technol Dept Chem Engn Delhi 110016 India

This paper presents a formulation of stochastic model predictive control (SMPC) for control of batch processes modeled by nonlinear Differential algebraic Equations (DAEs). In presence of additive stochastic disturbances, states become stochastic variables and measurements are also corrupted with noise. Hence, to obtain refined state estimates we employ DAE-Extended Kalman Filter (EKF). Linear prediction model obtained by successive linearization is used in the SMPC formulation. Our proposed SMPC solves a chance constrained optimization problem in receding horizon window to calculate future control moves with pre-specified degree of constraint violation for trajectory tracking. Further, the probabilistic constraints are converted to hard constraints by accommodating necessary back-off. We present a simulation case study of batch transesterification reactor modeled by non-linear DAEs to validate the proposed SMPC framework. (C) 2020, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control Successive linearization DAE-EKF batch processes

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stochastic model predictive control of Air Conditioning System for Electric Vehicles: Sensitivity Study, Comparison, and Improvement

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第9期14卷 4179-4189页

作者： He, Hongwen Jia, Hui Sun, Chao Sun, Fengchun Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China

A stochastic model predictive controller (SMPC) of air conditioning (AC) system is proposed to improve the energy efficiency of electric vehicles (EVs). A Markov-chain based velocity predictor is adopted to provide a sense of the future disturbances over the SMPC control horizon. The sensitivity of electrified AC plant to solar radiation, ambient temperature, and relative air flow speed is quantificationally analyzed from an energy efficiency perspective. Three control approaches are compared in terms of the electricity consumption, cabin temperature, and comfort fluctuation, which include the proposed SMPC method, a generally used bang-bang controller, and dynamic programming as the benchmark. Real solar radiation and ambient temperature data are measured to validate the effectiveness of the SMPC. Comparison results illustrate that SMPC is able to improve the AC energy economy by 12% compared to the rule-based controller. The cabin temperature variation is reduced by more than 50.4%, resulting with a much better cabin comfort.

关键词： Air conditioning (AC) comfort electric vehicle (EV) energy efficiency stochastic model predictive control

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stochastic model predictive control with active uncertainty learning: A Survey on dual control

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ANNUAL REVIEWS IN control 2018年 45卷 107-117页

作者： Mesbah, Ali Univ Calif Berkeley Dept Chem & Biomol Engn Berkeley CA 94720 USA

This paper provides a review of model predictive control (MPC) methods with active uncertainty learning. System uncertainty poses a key theoretical and practical challenge in MPC, which can be aggravated when system uncertainty increases due to the time-varying nature of system dynamics. For uncertain systems with stochastic uncertainty, this paper presents the stochastic MPC (SMPC) problem in the dual control paradigm, where the control inputs to an uncertain system have a probing effect for active uncertainty learning and a directing effect for controlling the system dynamics. The complexity of the SMPC problem with dual control effect is described in connection to stochastic dynamic programming as well as Bayesian estimation for its output feedback implementation. Further, implicit and explicit dual control methods for approximating the receding-horizon control problem with dual control effect are surveyed and analyzed with the intent to discuss the key challenges and opportunities in SMPC with dual control effect. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： stochastic optimal control stochastic model predictive control Active uncertainty learning Implicit dual control Explicit dual control

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A data-driven robust optimization approach to scenario-based stochastic model predictive control

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JOURNAL OF PROCESS control 2019年 75卷 24-39页

作者： Shang, Chao You, Fengqi Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

stochastic model predictive control (SMPC) has been a promising solution to complex control problems under uncertain disturbances. However, traditional SMPC approaches either require exact knowledge of probabilistic distributions, or rely on massive scenarios that are generated to represent uncertainties. In this paper, a novel scenario-based SMPC approach is proposed by actively learning a data-driven uncertainty set from available data with machine learning techniques. A systematical procedure is then proposed to further calibrate the uncertainty set, which gives appropriate probabilistic guarantee. The resulting data-driven uncertainty set is more compact than traditional norm-based sets, and can help reducing conservatism of control actions. Meanwhile, the proposed method requires less data samples than traditional scenario-based SMPC approaches, thereby enhancing the practicability of SMPC. Finally the optimal control problem is cast as a single-stage robust optimization problem, which can be solved efficiently by deriving the robust counterpart problem. The feasibility and stability issue is also discussed in detail. The efficacy of the proposed approach is demonstrated through a two-mass-spring system and a building energy control problem under uncertain disturbances. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control Chance constraints Scenario programs Robust model predictive control Machine learning

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Dynamic option hedging with transaction costs: A stochastic model predictive control approach

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2019年第15期29卷 5058-5077页

作者： Plessen, Mogens Graf Puglia, Laura Gabbriellini, Tommaso Bemporad, Alberto IMT Sch Adv Studies Lucca I-55100 Lucca Italy MPS Capital Serv Modelling & Quantitat Anal Siena Italy

This paper proposes stochastic model predictive control as a tool for hedging derivative contracts (such as plain vanilla and exotic options) in the presence of transaction costs. The methodology combines stochastic scenario generation for the prediction of asset prices at the next rebalancing interval with the minimization of a stochastic measure of the predicted hedging error. We consider 3 different measures to minimize in order to optimally rebalance the replicating portfolio: a trade-off between variance and expected value of hedging error, conditional value at risk, and the largest predicted hedging error. The resulting optimization problems require solving at each trading instant a quadratic program, a linear program, and a (smaller-scale) linear program, respectively. These can be combined with 3 different scenario generation schemes: the lognormal stock model with parameters recursively identified from data, an identification method based on support vector regression, and a simpler scheme based on perturbation noise. The hedging performance obtained by the proposed stochastic model predictive control strategies is illustrated on real-world data drawn from the NASDAQ-100 composite, evaluated for a European call and a barrier option, and compared with delta hedging.

关键词： financial options hedging techniques scenario generation stochastic model predictive control stochastic programming transaction costs

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Development and experimental validation of hierarchical energy management system based on stochastic model predictive control for Off-grid Microgrids

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ADVANCES IN APPLIED ENERGY 2021年 2卷

作者： Polimeni, Simone Meraldi, Lorenzo Moretti, Luca Leva, Sonia Manzolini, Giampaolo Politecn Milan Dipartimento Energia Via Lambruschini 4 I-20156 Milan Italy Engie EPS Via Grazzini 14 I-20159 Milan Italy

In this study, a hierarchical energy management system (EMS) is proposed, to coordinate different energy sources in an islanded multi-good microgrid. The first layer deals with the daily scheduling problem, while the second layer balances the generation in real-time. A novel second layer formulation, relying on model predictive control under a scenario-based stochastic approach (sMPC), is introduced and it is compared to a reference formulation, based on a central proportional-integral controller following the indications set by the first layer. The proposed sMPC explicitly accounts for uncertainty considering several scenarios of very-short term forecast errors, that act as disturbances for the system. The sMPC evaluates the control actions and the correction rules required to guarantee optimal operations through disturbance-feedback. The EMS is implemented in an experimental setup and tested for daily operations under a rolling horizon approach. The accuracy of the numerical system simulation is evaluated, resulting in an average discrepancy of 1.7%, in terms of operation cost, with respect to the experimental operations. Then, a test case comparing the proposed EMS with the reference approach shows that the adoption of sMPC allows to approach the lowest possible operation cost achievable by a second layer with an advantage of 2.7 % against the reference case. Finally, the developed sMPC leads to only 0.5% additional costs than an ideal controller working on the same control layer.

关键词： Energy management systems Off-grid Microgrid stochastic model predictive control MILP optimization

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Recursively feasible stochastic model predictive control using indirect feedback

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AUTOMATICA 2020年 119卷 109095-109095页

作者： Hewing, Lukas Wabersich, Kim P. Zeilinger, Melanie N. Swiss Fed Inst Technol Inst Dynam Syst & Control Zurich Switzerland

We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasible and guarantees chance constraint satisfaction for the closed-loop system, which is typically challenging for systems under unbounded disturbances. Under an i.i.d. zero-mean assumption, we provide an average asymptotic performance bound. A building control example illustrates the approach in an application with time-varying, correlated disturbances. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control Chance constraints predictive control

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A Sampling-and-Discarding Approach to stochastic model predictive control for Renewable Energy Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 7142-7147页

作者： Balázs Cs. Csáji Krisztián B. Kis András Kovács Institute for Computer Science and Control (SZTAKI) Budapest Kende utca 13-17 Hungary H-1111

关键词： stochastic model predictive control chance-constrained optimization partially controllable systems randomized methods scenario approach renewable energy system

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Successive Linearization based stochastic model predictive control for batch processes described by DAEs

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IFAC-PapersOnLine 2020年第1期53卷 380-385页

作者： Laxman Leema Atmaram Hariprasad Kodamana Department of Chemical Engineering Indian Institute of Technology Delhi India - 110016

关键词： stochastic model predictive control Successive linearization DAE-EKF batch processes

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