检索结果-内蒙古大学图书馆

IEEE Transactions on Automatic control 2025年

作者： Joa, Eunhyek Borrelli, Francesco University of California Model Predictive Control Lab Department of Mechanical Engineering Berkeley United States

We propose a model predictive control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of convex stage costs for constrained linear uncertain systems. The proposed method aims to enhance a given sub-optimal controller, leveraging data to achieve a nearly optimal solution for the infinite horizon problem. The method is built on two techniques. First, we estimate the expected values of the convex costs using a computationally tractable approximation, achieved by sampling across the space of disturbances. Second, we implement a data-driven approach to approximate the optimal value function and its corresponding domain, through systematic exploration of the system's state space. These estimates are subsequently used to calculate the terminal cost and terminal set within the proposed MPC. We prove recursive feasibility, robust constraint satisfaction, and convergence in probability to the target set. Furthermore, we prove that the estimated value function converges to the optimal value function in a local region. The effectiveness of the proposed MPC is illustrated with detailed numerical simulations and comparisons with a value iteration method and a Learning MPC that minimizes a certainty equivalent cost. © 1963-2012 IEEE.

关键词： predictive control systems

Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification

学校读者我要写书评

暂无评论

Eco-driving under localization uncertainty for connected veh...

IEEE Symposium on Intelligent Vehicle

作者： Eunhyek Joa Eric Yongkeun Choi Francesco Borrelli Department of Mechanical Engineering Model Predictive Control Lab University of California at Berkeley

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates 12% improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.

关键词： Location awareness Uncertainty Connected vehicles Intelligent vehicles Roads Optimal control Real-time systems

Approximate solution of stochastic infinite horizon optimal control problems for constrained linear uncertain systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Joa, Eunhyek Borrelli, Francesco Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley United States

We propose a model predictive control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of convex stage costs for constrained linear uncertain systems. The proposed method aims to enhance a given sub-optimal controller, leveraging data to achieve a nearly optimal solution for the infinite horizon problem. The method is built on two techniques. First, we estimate the expected values of the convex costs using a computationally tractable approximation, achieved by sampling across the space of disturbances. Second, we implement a data-driven approach to approximate the optimal value function and its corresponding domain, through systematic exploration of the system’s state space. These estimates are subsequently used to calculate the terminal cost and terminal set within the proposed MPC. We prove recursive feasibility, robust constraint satisfaction, and convergence in probability to the target set. Furthermore, we prove that the estimated value function converges to the optimal value function in a local region. The effectiveness of the proposed MPC is illustrated with detailed numerical simulations and comparisons with a value iteration method and a Learning MPC that minimizes a certainty equivalent cost. Copyright © 2024, The Authors. All rights reserved.

关键词： predictive control systems

Energy-efficient predictive control for connected, automated driving under localization uncertainty

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Joa, Eunhyek Choi, Eric Yongkeun Borrelli, Francesco The Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley United States

This paper presents a data-driven model predictive control (MPC) for energy-efficient urban road driving for connected, automated vehicles. The proposed MPC aims to minimize total energy consumption by controlling the vehicle's longitudinal motion on roads with traffic lights and front vehicles. Its terminal cost function and terminal constraints are learned from data, which consists of the closed-loop state and input trajectories. The terminal cost function represents the remaining energy-to-spend starting from a given terminal state. The terminal constraints are designed to ensure that the controlled vehicle timely crosses the upcoming traffic light, adheres to traffic laws, and accounts for the front vehicles. We validate the effectiveness of our method through both simulations and vehicle-in-the-loop experiments, demonstrating 19% improvement in average energy efficiency compared to conventional approaches that involve solving a long-horizon optimal control problem for speed planning and employing a separate controller for speed tracking. © 2024, CC BY.

关键词： predictive control systems

Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Joa, Eunhyek Choi, Eric Yongkeun Borrelli, Francesco The Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley United States

关键词： Vehicles

Learning for Online Mixed-Integer model predictive control with Parametric Optimality Certificates

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Russo, Luigi Nair, Siddharth H. Glielmo, Luigi Borrelli, Francesco The Model Predictive Control lab University of California Berkeley United States University of Sannio Italy

We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in model predictive control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) a candidate integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers. © 2023, CC BY.

关键词： model predictive control

Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第2期53卷 14224-14229页

作者： Yongkeun Choi Jacopo Guanetti Scott Moura Francesco Borrelli Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley CA 94720 USA Energy Controls and Applications Lab Department of Civil and Environmental Engineering University of California Berkeley CA 94720 USA

This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The onboard layer receives the learned policy parameters from the cloud layer and computes a real-time solution to the powertrain energy management problem, using a model predictive control scheme. The proposed EMS is evaluated on more than 3000 miles (48 independent driving cycles) of real-world trip data, collected along three commuting routes in California. For the routes, the proposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe when compared to a baseline EMS.

关键词： Data-based control Nonlinear predictive control Real-time control Engine modelling control Hybrid alternative drive vehicles Nonlinear optimal automotive control

Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Choi, Yongkeun Guanetti, Jacopo Moura, Scott Borrelli, Francesco Model Predictive Control Lab Department of Mechanical Engineering University of California BerkeleyCA94720 United States Energy Controls and Applications Lab Department of Civil and Environmental Engineering University of California BerkeleyCA94720 United States

This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The on-board layer receives the learned policy parameters from the cloud layer and computes a real-time solution to the powertrain energy management problem, using a model predictive control scheme. The proposed EMS is evaluated on more than 3000 miles (48 independent driving cycles) of real-world trip data, collected along three commuting routes in California. For the routes, the proposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe when compared to a baseline EMS. Copyright © 2020, The Authors. All rights reserved.

关键词： Real time control

Autonomous parking of vehicle fleet in tight environments

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Shen, Xu Zhang, Xiaojing Borrelli, Francesco Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley United States

The problem of autonomous parking of vehicle fleets is addressed in this paper. We present a system-level modeling and control framework which allows investigating different vehicle parking strategies while taking into account path planning and collision avoidance. The proposed approach decouples the problem into a centralized parking spot allocation and path generation, and a decentralized collision avoidance control. This paper presents the hierarchical framework and algorithmic details. Extensive simulations are used to assess several allocation strategies in terms of total fleet parking time and queue length. In particular, we describe how Braess's paradox can be observed for parking vehicle fleets. Copyright © 2019, The Authors. All rights reserved.

关键词： Collision avoidance

FERMENTATION AND GENETIC-ENGINEERING - PROBLEMS AND PROSPECTS

学校读者我要写书评

暂无评论

BIO-TECHNOLOGY 1983年第10期1卷 847-&页

作者： BULL, DN Daniel N. Bull Ph.D. is a consultant in fermentation technology and president of Satori Corporation P.O. Box 1730 Montclair N.J. 07042. (201) 783-9787.REFERENCES Graff G.M. Short H. and Keene J.1983. Gene-splicing methods move from lab to plant. Chem. Eng.90: 22-27.|ISI|Broda P.1979. p. 1-3. Plasmids. W. H. Freeman Oxford and San Francisco.Donoghue D.J. and Sharp P.A.1978. Construction of a hybrid bacteriophage-plasmid recombinant DNA vector. J. Bact.136: 1192-1196.|PubMed|ISI|ChemPort|Bok S.H. Hoppe D. Mueller D.C. and Lee S.E.1983. Improving the production of recombinant DNA proteins through fermentation development. Abstract from 186th ACS Natl. Mtg. Washington D.C. Sept. 1.Maniatis T. Fritsch E.F. and Sam-brook J.1982. p. 88. Molecular Cloning. Cold Spring Harbor Laboratory. Guidelines for research involving recombinant DNA molecules June 1983 Fed. Reg.48: 24556-24581. Modifications of physical containment recommendations for large-scale uses of organisms containing recombinant DNA molecules. 1983. Recomb. DNA Tech. Bull.6: 69-70.Bull D.N. Thoma R.W. and Stinnett T.E.1983. Bioreactors for submerged culture. In:Adv. in Biotechnological Proc. A. Mizrahi and A. L. van Wezel (eds.) 1: 1-30.Schmidli B.L. and Swartz R.W.1982. Design considerations for aseptic fermentation. Presentation at 184th ACS Natl. Mtg. Kanas City MO.Sittig W.1982. The present state of fermentation reactors. J. Chem. Tech. Biotechnol.32: 47-58.|ISI|Strek F.1963. Intl. Chem. Eng.3: 533.Uhl V.W. and Gray J.B.1996. Mixing Theory and Practice Vol. I. Academic Press New York.Peters M.S. and Timmerhaus K.D.1968. p. 542. Plant Design and Economics for Chemical Engineers. McGraw-Hill New York.Dickey D.S. and Hicks R.W. Fundamentals of agitation. Chem. Eng.83: 93-100.Oldshue J.Y.1983. Fluid mixing technology and practice. Chem. Eng.90: 82-108.Kipke K.D.1981. Heat transfer in aerated non-Newtonian fluids. Abstract from 2nd Eur. Cong. Biotech. Eastbourne UK April 5-10.Blakebrough N. McM