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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Ajayi, Adedayo Luk, Patrick Lao, Liyun Khan, Mohammad Farhan Luo, Zhenhua Cranfield University United Kingdom University of Roehampton United Kingdom

ISBN: (纸本)9781665471640

The integration of renewable energy sources, electric vehicles, and energy storage systems in microgrids introduces a higher level of uncertainty and variability in energy generation and consumption patterns. stochastic model predictive control (SMPC) has emerged as a promising technique for optimizing the operation of microgrids due to its ability to account for uncertainties, constraints, and dynamic interactions among various components. The paper evaluates the integration of wind and solar energy and electric ground support vehicle batteries (EGSVBs) into the green airport energy system. A mixed integer linear programming (MILP) algorithm is used to optimize the power flow between sources in the microgrid and EGSVBs, which can deliver surplus power back to the utility grid and charge and discharge EVBs based on demand. The study implements a novel approach, "stochastic model predictive control based Cascade Feed-forward Neural Network (SMPC-CFFNN)", on higher-order complex dynamic mathematical models for energy management and integrates the stochastic nature of renewable power generation. The effectiveness of the proposed SMPC-CFFNN approach is examined under different operating conditions and compared with the conventional SMPC controller. The results demonstrate that the proposed control strategy minimizes computing complexity and optimization time, and a zero-emission-based microgrid for a resilient green airport is achievable with the proposed energy management architecture. © 2023 IEEE.

关键词： Artificial neural network cascade feed-forward mixed-integer linear programming (MILP) stochastic model predictive control

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Evaluation of Energy Saving Potential using stochastic model predictive control for Stand Alone Air Conditioning Units A Study in Indian Scenario

Evaluation of Energy Saving Potential using Stochastic Model...

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1st International Conference on Non Conventional Energy (ICONCE)

作者： Ray, Tiyasa Majumdar, Sandeepan Mukherjee, Sumit Jadavpur Univ Dept Architecture Dept Power Engn Kolkata India Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781479933396

Reduction in energy consumption has become imperative in the modern day. The building segment is responsible for almost 40% consumption. These installations are typically located at the distribution level. There are losses associated with the transmission system, such as T&D Losses and pilferage losses. Hence a single unit of energy saved at distribution level would amount to greater savings at the generation level. Developing nations rely largely on standalone Air Conditioning units for office and domestic use since these facilities rarely designed to accommodate centralized Heating ventilation and Air Conditioning (HV AC) systems. In this paper a novel approach to control all these air conditioning units using a centralized controller based on stochastic model predictive control (SMPC) has been presented. The SMPC takes into account the predicted weather to reduce energy consumption while maintaining the comfort level of the occupants. A sample office space has been modeled and performance of the algorithm has been studied for weather conditions of large cities of India. With centralized SMPC the system has significantly outperformed the existing SAC with localized controller.

关键词： Building Energy Building modeling Energy Saving stochastic model predictive control

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Generalised polynomial chaos expansion approaches to approximate stochastic model predictive control^†

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INTERNATIONAL JOURNAL OF control 2013年第8期86卷 1324-1337页

作者： Kim, Kwang-Ki K. Braatz, Richard D. Univ Illinois Urbana IL 61801 USA MIT Cambridge MA 02139 USA

This paper considers the model predictive control of dynamic systems subject to stochastic uncertainties due to parametric uncertainties and exogenous disturbance. The effects of uncertainties are quantified using generalised polynomial chaos expansions with an additive Gaussian random process as the exogenous disturbance. With Gaussian approximation of the resulting solution trajectory of a stochastic differential equation using generalised polynomial chaos expansion, convex finite-horizon model predictive control problems are solved that are amenable to online computation of a stochastically robust control policy over the time horizon. Using generalised polynomial chaos expansions combined with convex relaxation methods, the probabilistic constraints are replaced by convex deterministic constraints that approximate the probabilistic violations. This approach to chance-constrained model predictive control provides an explicit way to handle a stochastic system model in the presence of both model uncertainty and exogenous disturbances.

关键词： stochastic model predictive control generalised polynomial chaos expansions chance constraints Boole's inequality convex relaxation semidefinite programming stochastic differential equations

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stochastic model predictive controller for wind farm frequency regulation in waked conditions

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期211卷

作者： Chen, Sunny Mathieu, Johanna L. Seiler, Peter Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48103 USA

As renewable energy penetrations increase, wind farms may be required to provide ancillary services such as frequency regulation. For wind farms, two issues complicate frequency regulation: turbine-wake interactions alter the available wind power, and the future power reference signal is uncertain. This paper presents a stochastic model predictive controller (MPC) for wind farms to track an uncertain frequency regulation signal while accounting for wakes. The controller uses a simplified Park model with wake propagation delay to predict turbine-wake interactions. The controller operates without any knowledge of the future power regulation signal or wind speed. The controller solves a nonconvex stochastic optimization program using scenario optimization. Our stochastic MPC tracks with 7%-10% less error than our deterministic MPC controllers.

关键词： Frequency regulation stochastic model predictive control Wind farm control

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Adaptively Constrained stochastic model predictive control for Closed-Loop Constraint Satisfaction

Adaptively Constrained Stochastic Model Predictive Control f...

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American control Conference (ACC)

作者： Oldewurtel, Frauke Sturzenegger, David Esfahani, Peyman Mohajerin Andersson, Goeran Morari, Manfred Lygeros, John Swiss Fed Inst Technol Zurich ETHZ Dept Elect Engn Power Syst Lab Zurich Switzerland

ISBN: (纸本)9781479901784

stochastic model predictive control (SMPC) for discrete-time linear systems subject to additive disturbances with chance constraints on the states and hard constraints on the inputs is considered. Current chance constrained MPC methods-based on analytic reformulations or on sampling approaches-tend to be conservative partly because they fail to exploit the predefined violation level in closed-loop. For many practical applications, this conservatism can lead to a loss in performance. We propose an adaptive SMPC scheme that starts with a standard conservative chance constrained formulation and then on-line adapts the formulation of constraints based on the experienced violation frequency. Using martingale theory we establish guarantees of convergence to the desired level of constraint violation in closed-loop for a special class of linear systems. Comments are given on how to extend this to a broader class of (non-)linear systems. The developed methodology is demonstrated with an illustrative example.

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

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Towards data-driven stochastic predictive control

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第7期35卷 2588-2610页

作者： Pan, Guanru Ou, Ruchuan Faulwasser, Timm TU Dortmund Inst Energy Syst Energy Efficiency & Energy Econ Dortmund Germany TU Dortmund Inst Energy Syst Energy Efficiency & Energy Econ D-44227 Dortmund Germany

Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data-driven stochastic control. In this paper, we propose a data-driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data-driven surrogate optimal control problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data-driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.

关键词： closed-loop properties data-driven system representation polynomial chaos expansion stochastic model predictive control

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Safety-aware human-lead vehicle platooning by proactively reacting to uncertain human behaving

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 170卷

作者： Hu, Jia Wang, Shuhan Zhang, Yiming Wang, Haoran Liu, Zhilong Cao, Guangzhi Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha Peoples R China Dazhuo Intelligent Technol Co Shanghai Peoples R China

Human-Lead Cooperative Adaptive Cruise control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great limitation on driving safety due to the lack of considering the leading human driver's uncertain behavior. In this study, a HL-CACC controller is designed based on stochastic model predictive control (SMPC). It is enabled to predict the driving intention of the leading Connected Human-Driven Vehicle (CHV). The proposed controller has the following features: (i) enhanced perceived safety in oscillating traffic;(ii) guaranteed safety against hard brakes;(iii) computational efficiency for real-time implementation. The proposed controller is evaluated on a PreScan&Simulink simulation platform. Real vehicle trajectory data is collected for the calibration of the simulation. Results reveal that the proposed controller: (i) improves perceived safety by 19.17% in oscillating traffic;(ii) enhances actual safety by 7.76 % against hard brakes;(iii) is confirmed with string stability. The computation time is approximately 3.2 ms when running on a laptop equipped with an Intel i5-13500H CPU. This indicates the proposed controller is ready for real-time implementation.

关键词： CACC Vehicle platooning Human-lead platooning stochastic model predictive control

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Learning-enabled stochastic predictive control for nonlinear discrete-time step backward high-order fully actuated systems

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年

作者： Ning, Chao Zhao, Junhao Wang, Han Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In this paper, we seamlessly integrate machine learning techniques with stochastic model predictive control (MPC) to address the regulation problem of nonlinear discrete-time step backward High-Order Fully Actuated (HOFA) systems with additive disturbance. By exploiting the full-actuation characteristic of the HOFA system, we neatly eliminate the non-linearity of the system, thus circumventing the complex computation of uncertainty propagation in nonlinear stochastic MPC. To cope with the random disturbance, its probability distribution on each principal component is well captured from data based on principal component analysis, and the uncertainty bound is effectively estimated via kernel density estimation and quantile functions. Based upon such probabilistic information, we impose constraint tightening on the state limits and define terminal sets by drawing on the concept of tubes. On this basis, we employ stochastic MPC for receding horizon control of HOFA systems, of which the recursive feasibility and stability are proved theoretically. Finally, numerical experiments and an application to hydrogen electrolyzer temperature control are used to demonstrate the merits of the proposed approach in comparison with state-of-the-art methods.

关键词： High-order fully actuated model stochastic model predictive control principal component analysis kernel density estimation

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stochastic optimal control for autonomous driving applications via polynomial chaos expansions

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OPTIMAL control APPLICATIONS & METHODS 2024年第1期45卷 3-28页

作者： Listov, Petr Schwarz, Johannes Jones, Colin N. EPFL Automat Control Lab EPFL STI IGM LA3 Stat 9 CH-1015 Vaud Switzerland Tech Univ Munich Chair Automat Control TUM Sch Engn & Design Munich Germany

model-based methods in autonomous driving and advanced driving assistance gain importance in research and development due to their potential to contribute to higher road safety. Parameters of vehicle models, however, are hard to identify precisely or they can change quickly depending on the driving conditions. In this paper, we address the problem of safe trajectory planning under parametric model uncertainties motivated by automotive applications. We use the generalized polynomial chaos expansions for efficient nonlinear uncertainty propagation and distributionally robust inequalities for chance constraints approximation. Inspired by the tube-based model predictive control, an ancillary feedback controller is used to control the deviations of stochastic modes from the nominal solution, and therefore, decrease the variance. Our approach allows reducing conservatism related to nonlinear uncertainty propagation while guaranteeing constraints satisfaction with a high probability. The performance is demonstrated on the example of a trajectory optimization problem for a simplified vehicle model with uncertain parameters. image

关键词： autonomous driving polynomial chaos expansion pseudospectral collocation stochastic model predictive control

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A stochastic predictive Adaptive Cruise control System With Uncertainty-Aware Velocity Prediction and Parameter Self-Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13900-13913页

作者： Wang, Jieyu Gong, Xun Wang, Ping Wang, Yuhao Wang, Rong Guo, Lulu Hu, Yunfeng Chen, Hong Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China Jilin Univ Dept Control Sci & Engn Changchun 130012 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

Connectivity technologies in intelligent transportation systems offer unprecedented opportunities to enhance mobility, fuel economy, and safety for automotive systems. However, the uncertain driving behavior of surrounding vehicles in real-world traffic scenarios can significantly undermine these benefits. To tackle this challenge, this article develops a stochastic predictive-adaptive cruise control (P-ACC) system that effectively addresses uncertainties and automatically adapts to various driving scenarios. The proposed system employs a Gaussian process (GP)-based velocity predictor as its foundation, accurately capturing the driving dynamics of the preceding vehicle while accounting for prediction uncertainty using variances. The real-time feasibility is assessed in a dSPACE rapid prototyping system. In addition, the developed stochastic-model predictive control (S-MPC) approach incorporates the predicted velocity variance into the probabilistic chance constraints, conservatively narrowing the optimization space of the velocity planning domain, thereby enabling more reliable control. To further enhance the system's performance in adapting to different driving conditions, a scenario-based parameter self-learning (PSL) technique is introduced in the S-MPC controller, utilizing Bayesian optimization (BO). Finally, the performance of the proposed controller is comprehensively evaluated by leveraging a high-fidelity simulator and on-board actual vehicle testing data. Simulation results demonstrate that the proposed method achieved a boost in tracking performance and driving comfort while maintaining fuel-saving benefits.

关键词： Uncertainty-aware velocity prediction predictive adaptive cruise control stochastic model predictive control Bayesian optimization

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