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Real-Time Resilient Tracking Control for Autonomous Vehicles Through Triple Iterative approximate dynamic programming

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 1015-1028页

作者： Li, Wenyu Geng, Jiale Cheng, Yunqi Tang, Liye Duan, Jingliang Duan, Feng Li, Shengbo Eben Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intervent Brain Comp Interface & I Tianjin 300350 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Anhui Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

Enhancing control precision, mitigating external disturbances, and ensuring real-time responsiveness stand as the cornerstone of autonomous vehicle tracking endeavors, each of which intricately interwoven to uphold operational safety. In pursuit of addressing these issues, this paper presents a triple iterative control method inspired by approximate dynamic programming (ADP) tailored for real-time disturbance avoidance. The control framework orchestrates simultaneous iterations of value function, control policy, and disturbance policy, engineered to optimize tracking control amidst external disturbances cast as a zero-sum differential game, tackled adeptly through deep neural networks. Rigorous mathematical proof underpins its triple iteration, coupled with assurances of residual error convergence, solidifying its safety guarantee ability and algorithmic resilience. To validate its effectiveness, both numerical simulations and experiments on a real micro-vehicle platform were conducted. Results underscore the feasibility of this new method, showcasing its energy-saving capability and a four-times acceleration compared to conventional model predictive control (MPC) approaches when confronted with lateral disturbances. Notably, the single-step calculation time of this method on the Raspberry Pi is only 1.44ms, affirming its practical viability and real-world applicability.

关键词： Vehicle dynamics Real-time systems Iterative methods Trajectory tracking Safety Optimal control dynamic programming Trajectory Convergence Autonomous vehicles approximate dynamic programming autonomous vehicles neural network resilient tracking control

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A fix and optimize method based approximate dynamic programming approach for the strategic fleet sizing and delivery planning problem

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2025年第1期33卷 91-119页

作者： Aghazadeh, Duygu Ertogral, Kadir Univ Turkish Aeronaut Assoc Logist Management Dept Ankara Turkiye Qatar Univ Mech & Ind Engn Dept Doha Qatar

Logistics related costs constitute a major part in total cost of a product in general. Considering a company that delivers goods to its customers using its owned fleet, fleet ownership and operational costs together with the inventory costs compose the total logistics costs. In this study, we suggest an approximate dynamic programming algorithm, with a look ahead strategy, that uses the fix and optimize method as the imbedded heuristic for solving integrated fleet composition and replenishment planning problem. The total annual distribution cost factors considered in the problem are vehicle ownership costs, approximate routing costs, and inventory related costs. In this problem, we aim to minimize the total logistic cost by optimizing the fleet composition, replenishment patterns, and customers assigned to each vehicle in the fleet. We produced a set of reasonably large instances randomly and showed the efficacy of the suggested solution method.

关键词： Fix and optimize approximate dynamic programming Fleet sizing Replenishment planning

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An approximate dynamic programming Approach to Repeated Games with Vector Losses

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OPERATIONS RESEARCH 2024年第1期72卷 373页

作者： Kamble, Vijay Loiseau, Patrick Walrand, Jean Univ Illinois Dept Informat & Decis Sci Chicago IL 60607 USA Univ Grenoble Alpes Ctr Natl Rech Sci Inst Polytech Grenoble INRIALab Informat Grenoble F-38058 Grenoble France Max Planck Inst Software Syst D-66123 Saarbrucken Germany Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses. Among other applications, such vector-valued games prominently arise in the analysis of worst-case regret in repeated decision making in unknown environments, also known as the adversarial online learning framework. At the core of our approach is a characterization of the lower Pareto frontier of the set of expected losses that a player can guarantee in these games as the unique fixed point of a set-valued dynamic programming operator. When applied to the problem of worst-case regret minimization with discounted losses, our approach yields algorithms that achieve markedly improved performance bounds compared with off-the-shelf online learning algorithms like Hedge. These results thus suggest the significant potential of ADP-based approaches in adversarial online learning.

关键词： vector repeated games online learning approximate dynamic programming

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An approximate dynamic programming Approach to dynamic Stochastic Matching

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INFORMS JOURNAL ON COMPUTING 2024年第4期36卷 1006-1022页

作者： You, Fan Vossen, Thomas Univ Colorado Leeds Sch Business Boulder CO 80309 USA

dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming -based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs' optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near -optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching.

关键词： matching approximate dynamic programming kidney exchange ridesharing matchmaking

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An approximate dynamic programming approach for solving aircraft fleet engine maintenance problem: Methodology and a case study

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期321卷 958-973页

作者： Zhang, Miao Yang, Jingyuan Zhang, Chuwen He, Simai Liu, Huikang Wang, Jinshen Wang, Zizhuo Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai 200433 Peoples R China Univ Chicago Booth Sch Business Chicago IL 60637 USA Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China China Southern Airlines Co Ltd Guangzhou 510403 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Cardinal Operat Co Ltd Beijing 100102 Peoples R China

We consider a long-term engine maintenance planning problem for an aircraft fleet. The objective is to guarantee sufficient on-wing engines to reach service levels while effectively organizing shop visits for engines. However, complexity arises from intricate maintenance policies and uncertainty in engine deterioration. To address this problem, we propose a graph-based approach representing high-dimensional engine statuses and transitions. We then formulate the problem as a multi-stage stochastic integer program with endogenous uncertainty. We develop an approximate dynamic programming algorithm enhanced by dynamic graph generation and policy-sifting techniques so as to reduce the computational overhead in large problems. We demonstrate the efficacy of our method, compared with other popular methods, in terms of running time and solution quality. In the case study, we present an implementation in a real-world decision system in China Southern Airlines, in which the proposed method works seamlessly with other supporting modules and significantly improves the efficiency of engine maintenance management.

关键词： OR in airlines Engine maintenance Fleet management Multi-stage stochastic integer programming approximate dynamic programming

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Green transport fleet renewal using approximate dynamic programming: A case study in German heavy-duty road transportation

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 186卷

作者： Winkelmann, Jonas Spinler, Stefan Neukirchen, Thomas WHU Otto Beisheim Sch Management Chair Logist Management Burgpl 2 D-56179 Vallendar Germany Duisburg Intermodal Terminal GmbH Gaterweg 201 D-47229 Duisburg Germany

Governments and manufacturers are starting to enforce the European transport industry's transition to sustainable mobility. Meanwhile, transport companies have begun to set their own emissions goals. To achieve these sustainably, they must develop efficient policies to renew their fleets with alternative-fuel vehicles. However, since future trends in relevant parameters are highly uncertain, fleet managers struggle to make informed decisions. We formulate fleet renewal as a sequential optimization problem, considering multiple technologies and operational clusters. Vehicle purchase, sales, depreciation, fuel, carbon, and electric battery prices are modeled as stochastic variables. We propose approximate dynamic programming to calculate fleet renewal policies that achieve emissions goals while optimizing total costs of ownership. This approach is tested in a case study of a German logistics service provider. We investigate optimal timings of purchases and sales for a heavy-duty truck fleet, considering four drivetrain technologies. Our approach can guide decision making in various fleet renewal settings. By applying it to the case study, we derive important managerial implications. The mobility transition will significantly increase transport fleets' total cost of ownership. To minimize costs, companies should not move prematurely to low-emissions technologies, but hold vehicles for as long as possible to benefit from fewer purchases and sinking prices. The optimal policy depends on the distance driven. For short-distance operations, diesel trucks will remain the dominant technology in the next years, but will be replaced by battery electric trucks in the medium term. In the far future, trucks powered by electricity and hydrogen will be equally important.

关键词： Transportation Fleet renewal Mobility transition Decarbonization approximate dynamic programming Simulation-based optimization

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Optimizing autonomous electric taxi operations with integrated mobile charging services: An approximate dynamic programming approach

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APPLIED ENERGY 2025年 378卷

作者： Hu, Qinru Hu, Simon Shen, Shiyu Ouyang, Yanfeng Chen, Xiqun (Michael) Zhejiang Univ Inst Intelligent Transportat Syst Coll Civil Engn & Architecture Hangzhou Peoples R China Zhejiang Univ ZJU UIUC Inst Haining Peoples R China Zhejiang Prov Engn Res Ctr Intelligent Transportat Hangzhou Peoples R China Univ Illinois Dept Civil & Environm Engn Champaign IL USA

This paper focuses on optimizing the routing and charging schedules of an autonomous electric taxi (AET) system integrated with mobile charging services. In this system, a fleet of AETs provides on-demand ride services for customers, while mobile charging vehicles (MCVs) are deployed as a flexible complement to fixed charging stations, offering fast charging options for AETs. A dynamic programming model is developed to optimize the joint operations of AETs and MCVs, considering stochastics in customer demand, AET energy consumption, and charging station resources. The objective is to maximize the operator's overall profit over the entire planning horizon, including revenues from serving customer requests, travel costs, charging costs, and penalties associated with both fleets. To address the stochastic and dynamic nature of the problem, an approximate dynamic programming (ADP) approach, incorporating customized pruning strategies to reduce the state and decision space, is proposed. This approach balances immediate operational gains with future potential profits. A series of numerical experiments have been conducted to evaluate the effectiveness of the proposed model and algorithm. Results show that the ADP-based policy significantly improves system performance compared to classical myopic benchmarks.

关键词： Autonomous electric taxis Mobile charging vehicles dynamic electric vehicle routing problem Stochastic optimization approximate dynamic programming

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approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach☆

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AUTOMATICA 2024年 160卷

作者： He, Kanghui Shi, Shengling van den Boom, Ton De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies to overcome their individual limitations. The predictive control law for constrained linear quadratic regulation (CLQR) problems has been proven to be piecewise affine (PWA) while the value function is piecewise quadratic. We exploit these formal results from MPC to design an ADP method for CLQR problems with a known model. A novel convex and piecewise quadratic neural network with a local-global architecture is proposed to provide an accurate approximation of the value function, which is used as the cost-to-go function in the online dynamic programming problem. An efficient decomposition algorithm is developed to generate the control policy and speed up the online computation. Rigorous stability analysis of the closed-loop system is conducted for the proposed control scheme under the condition that a good approximation of the value function is achieved. Comparative simulations are carried out to demonstrate the potential of the proposed method in terms of online computation and optimality.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： approximate dynamic programming Reinforcement learning Model predictive control Value function approximation Neural networks Constrained linear quadratic regulation

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A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期315卷 454-469页

作者： Satic, U. Jacko, P. Kirkbride, C. Lancaster Univ Management Sch Lancaster LA1 4YX England Abdullah Gul Univ Fac Engn TR-38080 Kayseri Turkiye Berry Consultants Abingdon OX14 5EG England

We consider the dynamic and stochastic resource -constrained multi -project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite -horizon discrete -time Markov decision process and seeks to maximise the expected discounted long -run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision -maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule -based algorithm and a reactive genetic algorithm.

关键词： Project scheduling Markov decision processes approximate dynamic programming dynamic resource allocation dynamic programming

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An approximate dynamic programming Approach to Vehicle Platooning Coordination in Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 16536-16547页

作者： Xiong, Xi Wang, Maonan Sun, Dengfeng Jin, Li NYU Tandon Sch Engn New York NY 10012 USA Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Shanghai Jiao Tong Univ UM Joint Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the availability of pre-determined plans, which is not feasible in real-time scenarios. In this paper, we address this issue in time-dependent networks by formulating a Markov decision process at each junction, aiming to minimize travel time and fuel consumption. Initially, we analyze coordinated platooning without routing to explore the cooperation among controllers on an identical path. We propose two novel approaches based on approximate dynamic programming, offering suboptimal control in the context of a stochastic finite horizon problem. The results demonstrate the superiority of the approximation in the policy space. Furthermore, we investigate platooning in a network setting, where speed profiles and routes are determined simultaneously. To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation. We subsequently employ the aforementioned policy approximation to determine speed profiles, considering essential parameters such as travel times. Our simulation results in SUMO indicate that our method yields better performance than conventional approaches, leading to potential travel cost savings of up to 38% . Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.

关键词： Junctions Fuels Routing dynamic programming Costs Roads Real-time systems Vehicle platooning multi-agent systems approximate dynamic programming approximate dynamic programming Bellman equation Bellman equation Routing dynamic programming Costs Roads Real-time systems Vehicle platooning multi-agent systems approximate dynamic programming Bellman equation

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