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stochastic MPC Based Attacks on Object Tracking in Autonomous Driving Systems

IFAC-PapersOnLine 2023年第2期56卷 5001-5008页

作者： Sourav Sinha Mazen Farhood Crofton Department of Aerospace and Ocean Engineering Virginia Tech Blacksburg VA 24061 USA

Decision making in advanced driver assistance systems involves in general the estimated trajectories of the surrounding objects. Multiple object tracking refers to the process of estimating in real time these trajectories, leveraging for this purpose sensors to detect the objects. This paper deals with devising attacks on object tracking in automated vehicles. The vehicle is assumed to have a detection-based object tracking system that relies on multiple sensors and uses an estimator such as a Kalman filter for sensor fusion and state estimation. The attack goal is to modify the object's state estimated by the victim vehicle to put the vehicle in an unsafe situation. This goal is achieved by judiciously perturbing some or all of the sensor outputs corresponding to the object of interest over a desired horizon. A stochastic model predictive control (SMPC) problem is formulated to compute the sequence of perturbations, whereby hard constraints on the perturbations and probabilistic chance constraints on the object's state are imposed. The chance constraints ensure that some desired conditions for a successful attack are satisfied with a prespecified probability. Reasonable assumptions are then made to obtain a computationally tractable linear SMPC program. The approach is demonstrated on an adaptive cruise control system in a simulation environment, where successful sequential attacks are generated, leading the victim vehicle into dangerous driving situations including collisions.

关键词： Autonomous Vehicles Adversarial Attacks stochastic model predictive control

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Autonomous Driving Traffic control Based on Human-in-the-loop Decisions 33

Autonomous Driving Traffic Control Based on Human-in-the-loo...

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33rd Chinese control and Decision Conference (CCDC)

作者： Pang, Yan Zhang, Gaochang Xia, Hao Key Lab Adv Technol Vehicles Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Aeronaut & Astronaut Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

ISBN: (纸本)9781665440899

With the development of science and technology, autonomous driving vehicles have received a lot of attention. Before autonomous driving vehicles can completely replace human-driven vehicles, we will experience the situation where human-driven vehicles and autonomous driving vehicles appear on the road at the same time. It is particularly important for the autonomous vehicles to understand drivers' intentions well, make corresponding decisions, complete the driving task safely and effectively. In this regard, the human-in-the-loop control idea is considered, and a control strategy based on stochastic model predictive control is proposed. It is applied on two autonomous driving scenarios: lane merging and left-turn. In the simulation, safety, effectiveness and robustness of the proposed strategy are verified.

关键词： Human-in-the-loop stochastic model predictive control Chance Constraint

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Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings

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ENERGY 2022年第PartA期244卷 122693-122693页

作者： Li, Hangxin Wang, Shengwei Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Hong Kong Peoples R China Dept Bldg Environm & Energy Engn Hong Kong Peoples R China

model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and little is known about their relative performance, particularly in utilization of building flexibility-resources. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings with PV power generation and battery storage systems are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： model predictive control Shrinking horizon model predictive control stochastic model predictive control Forecast uncertainty Flexibility-resources Building energy systems

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Examining various control strategies for the nexus across water supply, power generation and environment systems in Hehuang, China

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JOURNAL OF HYDROLOGY 2022年第PartA期615卷

作者： Feng, Maoyuan Liu, Pan Zhou, Yuliang Zhang, Xiaoqi An, Rihui Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan 430072 Peoples R China Hubei Prov Collaborat Innovat Ctr Water Resources Wuhan 430072 Peoples R China Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China Peking Univ Coll Urban & Environm Sci Beijing 100871 Peoples R China

The propagation and accumulation of uncertainty within the water supply, power generation, and environment (WPE) nexus may induce unexpected failures. Effective control strategies are needed to reduce the failure risks. Optimal control of the WPE nexus is challenging because of the complex interconnections and the multiple stakeholders. This study employs stochastic dynamical systems to simulate interconnected WPE systems and compares the performance of centralized, decentralized, and hybrid control strategies. The WPE nexus within the Hehuang region of China is selected as a case study. The results show that all controllable agents are either selfish or generous, protecting or sacrificing their own security, respectively. Hybrid control is the most suitable strategy for realistic cases, because it balances the risks of selfish and generous agents. The manager's role is to balance or hedge the risks and costs across agents by adjusting their decision preferences. This involves changing the risks of generous agents and the control costs of selfish agents. The three control strategies have little impact on system bottlenecks, despite obvious changes in the trajectories of the highest and lowest risk contribution ratios. Overall, this study advances the understanding of different control strategies for the WPE nexus, helping to mitigate the failure risk and improve the management of real systems.

关键词： WPE nexus control strategies stochastic model predictive control Risk aversion Bottleneck shifts

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Multi-objective control of isolated power systems under different uncertainty approaches

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 32卷

作者： Chapaloglou, Spyridon Faanes, Andreas Varagnolo, Damiano Tedeschi, Elisabetta Norwegian Univ Sci & Technol Dept Elect Power Engn OS Bragstads Plass 2 E N-7034 Trondheim Norway Norwegian Univ Sci & Technol Dept Engn Cybernet OS Bragstads Plass 2 E N-7034 Trondheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Povo Italy Univ Padua Dept Informat Engn Via 8 Febbraio 2 I-35122 Padua Italy

This paper proposes and compares a set of multi-objective supervisory controllers for an isolated power system including a gas turbine operating in load following mode as a dominant source of generation, a battery energy storage system, and stochastic renewable generation. It analyzes their capability to coordinate the gas turbine and energy storage to provide isochronous speed control, achieving fast frequency regulation for the local grid, while the gas turbine can still operate with minimum deviation from its optimal loading and the storage system can follow a pre-scheduled reference state of charge trajectory. In more detail, we consider and compare different control strategies to handle model and disturbance uncertainties, including stochastic model predictive control under various parametrizations of the scenario approach, and robust control under the H-infinity paradigm. The various controllers are compared against a benchmark, i.e., a deterministic predictive control strategy. Instead of point-to-point comparisons for some arbitrary cases (i.e., worst-case or expected), empirical distributions of the controller's performance for the whole probability spectrum are derived, leading to more accurate and representative comparisons. The analyzed performance indicators are nominal dynamic performance, constraint violation probabilities, and expected system operation performance. The results indicate the clear superiority of stochastic control over both robust and deterministic control in dealing with both parametric and disturbance uncertainty. Moreover, choosing an appropriate parametrization is shown to be essential to achieve both good nominal performance and lower violations probability, indicating that the superiority of stochastic control comes with the drawback of needing user-defined tuning from the designer. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： stochastic model predictive control Scenario approach Robust control Isolated power systems Energy storage systems Convex optimization

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Collaborative strategy of dynamic wireless charging electric vehicles and hybrid power system in microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 143卷

作者： Zhou, Ze Liu, Zhitao Su, Hongye Zhang, Liyan Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou Peoples R China Wuhan Univ Technol Sch Automation Wuhan Peoples R China

Dynamic wireless charging can strengthen the connection between in-motion electric vehicles and microgrids. To ensure the coordination between electric vehicles with the dynamic wireless charging mode (DWCEVs) and hybrid power system in the microgrid, this paper proposes a collaborative strategy consisting of the two-layer control structure. The objective of the upper layer controller is to minimize the power exchange between the main grid and microgrid, while calculating the reference power of the lower layer controller. Then, the lower layer controller allocates charging power to individual DWCEVs according to the reference value optimized by the upper layer controller, while taking into account the charging requirements of different DWCEVs. In addition, we consider the uncertainties of photovoltaic arrays power generation, traffic flow, electric vehicle battery capacity, and consumed state of charge as well as the corresponding processing methods. According to the number of control variables, the upper controller adopts the regular optimal control, and the lower controller adopts the stochastic model predictive control. Finally, the case studies demonstrate the effectiveness of the proposed collaborative strategy.

关键词： Dynamic wireless charging Electric vehicles Hybrid power system Microgrid stochastic model predictive control

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Data-driven energy management of isolated power systems under rapidly varying operating conditions

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APPLIED ENERGY 2022年 314卷

作者： Chapaloglou, Spyridon Varagnolo, Damiano Marra, Francesco Tedeschi, Elisabetta Norwegian Univ Sci & Technol Dept Elect Power Engn Os Bragstads Plass 2 E N-7034 Trondheim Norway Norwegian Univ Sci & Technol Dept Engn Cybernet Os Bragstads Plass 2 E N-7034 Trondheim Norway Equinor R&T Elect Technol Dept Arkitekt Ebbels 10 N-7005 Trondheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Povo Italy Univ Padua Dept Informat Engn Via 8 Febbraio2 I-35122 Padua Italy

We propose an energy management algorithm for isolated industrial power systems that integrate uncertain renewable generation and energy storage. The proposed strategy is designed to ensure sustainable and costeffective operations by managing the energy flows in the grid, and is structured so to cope with: (1) high levels of renewable power penetration, and (2) load profiles characterized by non-smooth patterns and irregular events (i.e., events such as those occurring from connections/disconnections of large scale equipment, or from large wind speed ramps). The proposed algorithm leverages a stochastic economic model predictive control (MPC) scheme capable of dealing simultaneously with the dispatch and scheduling of the local generation units. More precisely, the scheme embeds a mixed-integer linear programming (MILP) optimal control policy formulation together with a stochastic programming approach. Moreover, the optimization problem accounts for multiple techno-economical objectives, such as minimization of operational costs, battery degradation, and non-utilized energy. We test the algorithm on a case study of an isolated offshore Oil & Gas platform producing energy onsite with conventional gas turbines and a local wind farm, while integrating a battery energy storage system. The results show that the proposed approach can issue ensemble predictions that successfully capture the potential irregular variations just by using recent past information of the associated random variable, even when no particular sudden events are anticipated in the near-future (i.e., step changes/trend reversals). In this way, the approach provides useful future information for the optimal management of the grid. This effect is numerically quantified via simulations that compare the performance of the proposed stochastic optimization approach against its deterministic MPC version in several realistic operating conditions. The empirical results suggest that the stochastic version leads

关键词： stochastic model predictive control Quantile regression Random forests Isolated power systems Offshore wind power Energy storage Mixed-integer linear programming Economic MPC

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A tractable approximation for stochastic MPC and application to mechanical pulping processes

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COMPUTERS & CHEMICAL ENGINEERING 2020年 141卷 106977-106977页

作者： Tian, Hui Prakash, Jagadeesan Zavala, Victor M. Olson, James A. Gopaluni, R. Bhushan Univ British Columbia Dept Chem & Biol Engn Vancouver BC V6T 1Z4 Canada Anna Univ Dept Instrumentat Engn Madras Inst Technol Campus Chennai 600044 Tamil Nadu India Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Pulp & Paper Ctr Vancouver BC V6T 1Z4 Canada

This paper develops a tractable approximation for stochastic model predictive control (SMPC). Under the proposed approach, we solve multiple deterministic MPC (DMPC) problems over individual scenarios of the uncertain variables to obtain a set of control policies and select from this candidate set a control input that yields the best approximation of the SMPC solution (i.e., yields the smallest statistical measure of the objective function (e.g., expected value) and of the constraints). This approach is a scenario decomposition scheme that overcomes tractability issues of SMPC (which solves problems that incorporate multiple scenarios all-at-once). Moreover, the approach enables flexible handling of complex statistical measures (e.g., medians, quantiles, and chance constraints) and enables prioritization of objectives and constraints (this is difficult to do with off-the-shelf optimization solvers). An application to a nonlinear mechanical pulping process demonstrates that the approach provides high quality solutions. We hypothesize that this is because the optimal SMPC policy lives in a space that is spanned by the control policies for the individual scenarios. Moreover, we note that a traditional DMPC policy corresponds to the policy of an individual scenario (the mean scenario is typically chosen). Consequently, the proposed approach can do no worse than DMPC and can be interpreted as an approach that seeks to find a DMPC policy that best approximates the SMPC policy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control Economics Pulp and paper

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stochastic MPC-Based Air-Fuel Ratio Regulation of Compressed Natural Gas Engines

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IEEE ACCESS 2020年 8卷 38826-38832页

作者： Yang, Jun Sun, Bo Shandong Univ Dept Control Sci & Engn Jinan 250061 Peoples R China Shandong Jiaotong Univ Dept Automot Engn Jinan 250357 Peoples R China

In this paper, the air-fuel ratio regulation problem of compressed natural gas (CNG) engines is considered by employing stochastic model predictive control (MPC) technology. A stochastic model predictive regulator based on a discrete-time dynamic model of CNG engines is proposed, taking into account the residual gas, and the closed-loop system is deduced to be stochastically stable. A numerical simulation is performed to demonstrate the effectiveness of the proposed control scheme under two working conditions. The simulation results show that the performance of the proposed stochastic model predictive regulator is better than that of the open-loop controller.

关键词： Engines stochastic processes Natural gas Atmospheric modeling Numerical models Vehicle dynamics stochastic model predictive control air-fuel ratio regulation compressed natural gas engines

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A stochastic optimal energy management strategy considering battery health for hybrid electric bus

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第13期234卷 3112-3127页

作者： Cui, Qinghu Du, Shangye Liu, Congzhi Zhang, Laigang Wei, Guoliang Liaocheng Univ Sch Mech & Automot Engn Liaocheng Shandong Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100000 Peoples R China

The problem of battery health coupled with energy management brings a considerable challenge to the hybrid electric bus. To address this challenge, three contributions are made to realize optimal energy management control while prolonging battery life. First, a semi-empirical aging model of lithium iron phosphate battery is built and identified by the data fitting method, based on the battery cycling test. Besides, a severity factor map is constructed by employing the proposed aging model to characterize the relative aging of the battery under different operating conditions. Second, to make the driver demand torque more appropriate for statistical prediction, a Markov chain is formulated to predict driving behavior and also a stochastic vehicle mass estimation method is proposed to assist the prediction of required torque. Then, a stochastic multi-objective optimization problem is formulated by taking the severity factor map as a battery degradation criterion, where minimized battery degradation and fuel consumption can be simultaneously realized. Finally, a stochastic model predictive control strategy that considers battery health is established. Both simulation and hardware-in-loop tests are performed. The results demonstrate that fuel economy and battery degradation can be improved by 16.73% and 13.8% compared with rule-based strategy, respectively.

关键词： Battery aging model energy management hybrid electric bus multi-objective optimization stochastic model predictive control

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