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Risk-Aware Energy Management for Drive Mode control in Plug-in Hybrid Electric Vehicles

IEEE ACCESS 2022年 10卷 103619-103631页

作者： Watanabe, Ryunosuke Ibuki, Tatsuya Sakayanagi, Yoshihiro Funada, Riku Sampei, Mitsuji Tokyo Inst Technol Tokyo 1528550 Japan Meiji Univ Sch Sci & Technol Kawasaki Kanagawa 2148571 Japan Toyota Motor Co Ltd Susono City Shizuoka 4101193 Japan

This paper proposes risk-aware energy management for drive mode control in plug-in hybrid electric vehicles (PHEVs) to reduce fuel consumption. This paper focuses on reducing fuel usage in scenarios in which energy demands along a planned route are stochastically estimated using historical driving data. In this scenario, the proposed method evaluates the risk of high energy consumption based on the conditional value at risk (CVaR) and entropic value at risk (EVaR) derived from Chernoff's inequality. The CVaR quantifies the high fuel consumption expected in the tail of the probability distributions as a cost function. In contrast, the EVaR bound constraint provides stochastic constraints of electricity capacity based on the property of the cumulant-generating function. Each risk evaluation is formulated as a mixed-integer exponential cone programming problem by expressing the drive modes of a PHEV as binary variables. The proposed method was demonstrated using a detailed vehicle simulator with real-world driving cycles. The designed controller achieved, on two selected routes, 8.60% and 16.09% improvements on average, and 10.75% and 12.85% reductions at the 75th percentile compared to a commercial method. The simulations indicated that we can design the controller characteristics by adjusting the risk-awareness of energy loss. The conservativeness of the risk evaluation is also discussed based on the simulation results.

关键词： Energy management Reactive power Tail Random variables Probability distribution Plug-in hybrid electric vehicles Switches Energy management optimization-based control plug-in hybrid electric vehicles risk measurement

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Cooperative Path Following with Collision Avoidance Guarantees Using control Lyapunov and Barrier Functions 15th

Cooperative Path Following with Collision Avoidance Guarante...

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15th APCA International Conference on Automatic control and Soft Computing (controlO)

作者： Reis, Matheus F. Anand, Pallov Aguiar, A. Pedro Univ Porto Fac Engn Dept Elect & Comp Engn Res Ctr Syst & Technol P-4200465 Porto Portugal

ISBN: (纸本)9783031100475;9783031100468

In this paper, we address the problem of safety-critical, cooperative path following (CPF) control for a swarm of heterogeneous robotic vehicles following possibly intersecting paths. control Lyapunov Functions (CLFs) and control Barrier Functions (CBFs) are utilized on a Quadratic Program (QP) based controller to achieve the desired task, while also giving explicit collision avoidance guarantees. We propose a modified CLF-CBF based QP controller to overcome deadlock-like configurations by assigning specific priorities for each of the agents. Numerical simulation results for three agents shows the efficacy of the proposed methodology.

关键词： Lyapunov methods optimization-based control Cooperative path following

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A flexible optimization framework for district systems based on topological graph and hybrid models

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JOURNAL OF PROCESS control 2021年 107卷 27-36页

作者： Manganini, Giorgio Riverso, Stefano Kouramas, Kostantinos Gran Sasso Sci Inst GSSI Laquila Italy United Technol Res Ctr Ireland Cork Ireland

This paper proposes a scalable and widely replicable framework for the energy management of district systems. The framework is built on a graph-based energy characterization of the system, whose supply and demand dynamics are numerically represented by hybrid mathematical models involving the available control inputs and measurable outputs. Following the graph topology connecting supply and demand nodes, a suitable optimization problem is formulated to take into consideration the actual availability of the involved control input and measurements, resulting in a computationally tractable mixed-integer linear control problem which can be implemented in a receding-horizon fashion. As an illustrative case, the framework is applied to a real-case district heating system, and the validity of the approach is showed through model-in-the-loop and system-in-the-loop numerical results. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： District energy management optimization-based control Topological graph Hybrid systems

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Nonlinear Model Predictive control of a Particulate Polysilicon Reactor System for Enhanced Solar Cell Production

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IFAC-PapersOnLine 2024年第14期58卷 531-537页

作者： Carlos Veloz Davood B. Pourkargar Tim Taylor Department of Chemical Engineering Kansas State University Manhattan KS 66506 USA

A predictive modeling framework for silicon production in fluidized bed reactors is proposed to characterize the particle size distribution of the product and the powder loss. Two different flow regime modeling approaches are considered to describe the silane pyrolysis reaction and characterize the deposition rate that contributes to particle growth. A discrete population balance equation is used to estimate the particle size distribution as a function of the deposition rate. A nonlinear model predictive control is then utilized to regulate the system at the desired operating conditions. Detailed open-loop and closed-loop simulation studies demonstrate the successful integration of nonlinear MPC and the proposed predictive modeling approach.

关键词： control of particulate processes nonlinear process control model predictive optimization-based control industrial applications of process control process modeling identification

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Catching Flying Ball with Drone Using Monte Carlo Model Predictive control method

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IFAC-PapersOnLine 2024年第18期58卷 329-334页

作者： LIUYI ZHU Hisashi DATE Graduate School of Science and Technology University of Tsukuba 1-1-1 Tennodai Tsukuba Institute of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Tsukuba

In some sporting events, including baseball, the ball few into the spectator area, resulting in serious injuries. Having witnessed such situations, we felt that the safety of the spectator environment needed to be improved. The aim is, therefore, to prevent such accidents by utilizing drones to catch flying balls. Monte Carlo Model Predictive control (MCMPC) is a Monte Carlo-based control method. MCMPC is sample-based and does not require a gradient in the cost function, allowing discontinuous events such as collisions with obstacles to be incorporated into the predictive model. This study aims to utilize MCMPC to reduce the risk of drone collision after successful flying ball capture.

关键词： Model Predictive optimization-based control Monte Carlo methods UAV

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Data-Driven Design of Predictive Functional control based Feed-Forward Disturbance Rejection controller

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IFAC-PapersOnLine 2024年第14期58卷 610-615页

作者： Yoichiro Ashida Masanobu Obika National Institute of Technology Matsue College 14-4 Nishi-Ikuma Cho Matsue Japan ADAPTEX Co. Ltd. 13-60 Kagamiyama 3 Higashi-Hiroshima Hiroshima Japan

Many large-scale multi-input multi-output systems are treated as a combination of single-input single-output systems in reality. At such times, interference from input signals not focused is regarded as observable disturbances. For observable disturbances, feed-forward controllers are effective in rejecting the influence. A simple feed-forward controller construction is a combination of transfer functions of controlled and disturbance systems. This paper proposes an extension of the simple feed-forward controller and its parameter-tuning method. The controller is designed based on a Predictive Functional controller (PFC), one of the Model Predictive control (MPC). The effectiveness of the proposed scheme is verified by some simulation examples.

关键词： process control predictive control disturbance rejection data-driven control model predictive optimization-based control

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Distributed Estimation and control of Process Networks using Adaptive Community Detection

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IFAC-PapersOnLine 2024年第14期58卷 754-760页

作者： Amirmohammad Ebrahimi Davood B. Pourkargar Tim Taylor Department of Chemical Engineering Kansas State University Manhattan KS 66506 USA

An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The proposed approach employs the weighted graph representation of the process network model to identify optimal communities for distributed estimation and control architectures. The resulting decomposition dynamically adapts as the network transitions across different operating conditions. Consequently, adjustments are made to the integrated DMHE and DMPC architecture to optimize closed-loop performance and enhance robustness. A benchmark benzene alkylation process under various operating conditions is employed to substantiate the proposed methodology’s efficacy. Simulation results demonstrate the effectiveness of the proposed method, showing improved closed-loop performance and computational efficiency compared to traditional unweighted hierarchical community detection-based decompositions.

关键词： Nonlinear process control model predictive optimization-based control distributed control estimation graph-based methods for networked systems process control

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Supervisory multi-model control for supply chains with large uncertainty on the perishability rate

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IFAC-PapersOnLine 2024年第19期58卷 421-426页

作者： Beatrice Ietto Valentina Orsini Weizenbaum Institute Berlin Germany Department of Information Engineering Universita’ Politecnica delle Marche Ancona Italy

Defining an efficient inventory management of Perishable Supply Chains (PSCs) becomes a very difficult problem when the ”a priori” information on the perishability rate is affected by a large degree of uncertainty. In this paper we face this problem using a multi-model based supervisor and a reconfigurable min-max Robust Model Predictive control (RMPC). The twofold task of the supervisor is: 1) identify the most likely uncertainty scenario, 2) drive towards it the current min-max MPC law.

关键词： supply chain management inventory control control optimization of supply chains model predictive optimization-based control uncertain systems

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Study of the Application of Blender for Simulation of a Closed-loop Image-based Greenhouse Supplemental Lighting control

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IFAC-PapersOnLine 2024年第14期58卷 519-524页

作者： Kip Nieman Helen Durand Wayne State University 42 W. Warren Ave. Detroit MI 48202 USA

Image-based control and sensing has been applied in a wide variety of next generation manufacturing fields. Utilizing methods of simulating closed-loop image-based control may be advantageous for improving control performance and design without the need for an experimental setup. One software capable of these simulations is the open-source 3D modeling software Blender, which has many capabilities aided by a Python API. This work explores the use of Blender as an image-based control test bed, where both the process and the controller are simulated, in the context of a greenhouse supplemental lighting control system.

关键词： Simulation tools Greenhouse control Model predictive optimization-based control Digital twin development Modeling Blender Ray tracing 3D imaging

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Lyapunov-based Cyberattack Detection for Distinguishing Between Sensor and Actuator Attacks

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IFAC-PapersOnLine 2024年第14期58卷 604-609页

作者： Dominic Messina Helen Durand Wayne State University Detroit MI 48202 USA

control-theoretic cyberattack detection strategies are control strategies where control theory can be used in the design of the detection policies and analysis of stability properties with and without cyberattacks. This work provides a step toward understanding how to diagnose cyberattacks using control-theoretic cyberattack detection mechanisms. Specifically, we analyze the conditions under which a control-theoretic cyberattack detection strategy developed in our prior work to handle detection of simultaneous actuator and sensor attacks can be extended to distinguish between whether attacks are occurring on sensors or actuators. We present and evaluate heuristic concepts for attempting to diagnose sensor attacks; these again demonstrate the utility of control-theoretic diagnosis policies and lead to further suggestions for such control-theoretic policies.

关键词： Cyber physical system Advanced process control Model predictive optimization-based control Nonlinear predictive control Sensors actuators

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