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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Abouheaf, Mohammed Gueaieb, Wail Miah, Md Suruz Spinello, Davide Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Bradley Univ Dept Elect & Comp Engn Peoria IL USA Univ Ottawa Dept Mech Engn Ottawa ON Canada

ISBN: (纸本)9781728185262

Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals. This enforces several difficult-to-solve constraints that are associated with the error dynamical equations using classical optimal tracking and adaptive control approaches. An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems with partial prior knowledge of the system dynamics. The actuation forces are decided using innovative forms of temporal difference equations relevant to the vessel's surge and angular velocities. The solution is implemented using an online value iteration process which is realized by employing means of the adaptive critics and gradient descent approaches. The adaptive learning mechanism exhibited well-functioning and interactive features in react to different desired reference-tracking scenarios.

关键词： approximate dynamic programming Integral Reinforcement Learning Adaptive Critics Underactuated Vessels

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Large-Scale Optimization for Green Logistics and Stochastic Resource Allocation for Food Security

Large-Scale Optimization for Green Logistics and Stochastic ...

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作者： Alkhannan Alkaabneh, Faisal M. M. Cornell University

学位级别：Ph.D., Doctor of Philosophy

We studied several important management and policy analysis problems in food supply chain systems utilizing large-scale optimization, stochastic resource allocation, and data-analytics methodologies. We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their profit and minimize the costs due to fuel consumption, inventory holding, and greenhouse gas emissions (Chapter 2); 2) what is the best environmental intervention policy to reduce the environmental externalities associated with the production of fruits and vegetables considering environmental and economic dimensions simultaneously (Chapter 3); and (3) How can food banks better manage food supplies distribution to combat food insecurity of underserved population (Chapters 4 & 5). Specifically, we have explored the following four dimensions in food supply chains 1) Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. We consider the problem of inventory routing in the context of perishable products and find near-optimal replenishment scheduling and vehicle routes. To solve the problem efficiently, we develop an exact method based on Benders decomposition to find high-quality solutions in reasonable time and a two-stage meta-heuristic. 2) A systems approach to carbon policy for fruit supply chains: carbon tax, technology innovation, or land sparing? Reducing carbon emissions of food supply chains has increasingly received attention from businesses and policymakers. In order to propose sound policies aimed at lowering such emissions, policy makers favor tools that are informative in the economic and environmental dimensions simultaneously. In this study we offer a systems-based approach which is intended to do just that by developing a spatially and temporally disaggregated price equilibrium mathematical model for a food production and distribution system and a

关键词： approximate dynamic programming Data analytics Food banks Large-scale optimization Supply chain

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UAV Formation Shape Control via Decentralized Markov Decision Processes

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ALGORITHMS 2021年第3期14卷

作者： Azam, Md Ali Mittelmann, Hans D. Ragi, Shankarachary South Dakota Sch Mines & Technol Elect Engn Rapid City SD 57701 USA Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

关键词： swarm intelligence formation control decentralized Markov decision process approximate dynamic programming

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dynamic Resource Management Algorithms for Complex Systems and Novel Approaches to Adaptive Kalman Filtering

Dynamic Resource Management Algorithms for Complex Systems a...

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作者： Zhang, Lingyi University of Connecticut

学位级别：Ph.D., Doctor of Philosophy

This thesis considers three combinatorial optimization problems of substantial practical importance. First, a new approach to efficiently obtain a large number of ranked solutions to a 3-dimensional assignment problem is presented, and is applied to generate fuel assembly loading patterns. Second, we formulate the problem of dynamically scheduling maritime surveillance assets, and solve it using branch-and-cut and approximate dynamic programming (ADP) with rollout, and investigate the tradeoffs between the two. Third, a multi-objective ship routing problem is also investigated, where we propose a solution combining approximate dynamic programming techniques and clustering techniques to contain the computational and storage complexity. Lastly, this dissertation develops a seminal approach to adaptive Kalman filtering via the use of post-fit residuals given data samples -- an approach not yet discussed prior to this work.

关键词： Kalman filter optimization algorithms covariance estimation noise covariance approximate dynamic programming rollout branch-and-cut

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Multi-sourcing under supply uncertainty and Buyer's risk aversion

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2021年 9卷

作者： Chintapalli, Prashant Western Univ Ivey Business Sch London ON N6A 3K7 Canada

We address the combined problem of supplier (or vendor) selection and ordering decision when a buyer can choose to procure from multiple suppliers whose yields are uncertain and potentially correlated. We model this problem as a stochastic program with recourse in which the buyer purchases from the suppliers in the first period and, if needed, chooses to purchase from the spot market or from the suppliers with excess supply, whichever is beneficial, in the second period in order to meet the target procurement quantity. We solve the above problem using sample average approximation (SAA) technique that enables us to solve the problem easily in practice. We compare the performance of our solution with the certainty equivalent problem, which is practiced widely and which we use as the benchmark, to evaluate the efficacy of our approach. Next, we extend our model to incorpo-rate buyer's risk aversion with respect to the quantity procured. We reformulate the multi-sourcing problem as a mixed integer linear program (MILP) and adopt a statistical approach to account for buyer's risk aversion. Thus, we design a simple computational technique that provides an optimal sourcing policy from a set of suppliers when each supplier's yield is uncertain with a generic probability distribution.

关键词： Purchasing Supplier/vendor selection Supply/yield uncertainty approximate dynamic programming Stochastic programming with recourse

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Economic Dispatch of an Integrated Microgrid Based on the dynamic Process of CCGT Plant

Economic Dispatch of an Integrated Microgrid Based on the Dy...

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第33届中国控制与决策会议

作者： Zhiyi Lin Chunyue Song Jun Zhao Chao Yang Huan Yin College of Control Science and Engineering Zhejiang University

Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed *** dynamic energy flows in cogeneration units present challenges to the energy management of the *** this paper,a novel approximate dynamic programming(ADP) approach is proposed to solve this problem based on value function approximation,which is distinct with the consideration of the dynamic process constraints of the combined-cycle gas turbine(CCGT) ***,we mathematically formulate the multi-time periods decision problem as a finite-horizon Markov decision *** deal with the thermodynamic process,an augmented state vector of CCGT is ***,the proposed VFA-ADP algorithm is employed to derive the near-optimal real-time operation *** addition,to guarantee the monotonicity of piecewise linear function,we apply the SPAR algorithm in the update *** validate the effectiveness of the proposed method,we conduct experiments with comparisons to some traditional optimization *** results indicate that our proposed ADP method achieves better performance on the economic dispatch of the microgrid.

关键词： Microgrid dynamic Process Combined-Cycle Gas Turbine approximate dynamic programming

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Online Reinforcement Learning and Adaptive Control for Networked 2-DOF Helicopters Using Smartphones and Embedded Computers

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2025年

作者： Miah, Md Suruz Vonckx, Kenneth Janiak, Glenn Bradley Univ Dept Elect & Comp Engn 1501 W Bradley Ave Peoria IL 61625 USA

This paper contributes to the development of an online learning and adaptive control law for networked 2-degree-of-freedom (2-DOF) laboratory helicopters, utilizing smartphones and embedded computers. The primary objective is to address the challenges associated with controlling motion trajectories, specifically time-varying pitch and yaw angles, of fixed-base helicopters with partially known dynamics. These challenges are further amplified when coordinating multiple helicopters simultaneously using onboard embedded computer and a smartphone application. The proposed methodology employs reinforcement learning (RL) formulated as an approximate dynamic programming (ADP) problem to design an adaptive control strategy. Unlike conventional control approaches, such as linear quadratic regulators (LQR), H-infinity control, or proportional-integral-derivative control, that often assume complete knowledge of system dynamics or are validated in simulations or single-helicopter setups, the ADP technique introduced in this work leverages real-time state measurements to compute actuator commands. This enables effective tracking of reference motion trajectories without relying on full system dynamics. Additionally, the ADP framework is augmented by implementing an optimal output tracking and a linear quadratic regulator controller as benchmarks for comparative analysis. Experimental results demonstrate the efficacy of the proposed RL-based ADP control strategy in achieving accurate trajectory tracking for multiple networked helicopters in real-time. The performance comparison highlights significant improvements in adaptability and robustness over the benchmark controller. This work contributes to the field of intelligent control by demonstrating a practical and scalable approach to real-time motion trajectory control for networked multi-agent helicopter systems, validated through embedded computing and smartphone integration.

关键词： 2-DOF helicopter approximate dynamic programming Embedded computer Optimal control Smartphone

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A Risk-Averse Stochastic dynamic programming Approach to Energy Hub Optimal Dispatch

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 2169-2178页

作者： Moazeni, Somayeh Miragha, Amir H. Defourny, Boris Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Nanyang Technol Univ ERI N Energy Res Inst Singapore 637141 Singapore Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

This paper studies the optimal operation problem of an energy hub with multiple energy sources to serve stochastic electricity and heat loads in the presence of uncertain prices as well as operational constraints, such as minimum uptime and downtime requirements. Price and demand uncertainties are modeled by stochastic processes. The goal is to minimize some risk functional of the energy hub operational cost. A stochastic dynamic optimization formulation is introduced for the problem. An approximate dynamic programming framework, based on cost function approximation, is proposed to obtain dynamic dispatch policies. The approach enables a risk-sensitive energy hub operator to consider a non-differentiable risk measure and various constraints. The performance of the approach for the energy hub dispatch problem and characteristics of the storage levels are numerically investigated.

关键词： Energy hub stochastic optimization approximate dynamic programming risk

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Logistical Planning for Electric Vehicles Under Time-Dependent Stochastic Traffic

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第10期20卷 3771-3781页

作者： Bi, Xiaowen Tang, Wallace K. S. City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commercial usage. In this paper, a dynamic electric vehicle routing problem (D-EVRP) model is designed for planning the itinerary for goods delivery by the utilization of EVs in logistics industry. To reflect the real situation, the D-EVRP considers a time-dependent stochastic traffic condition and captures the discharging/charging pattern of an EV using an analytical battery model. Its aim is to minimize the overall service duration, subject to a variety of the state-of-art constraints common in EV routing problems. Furthermore, to address the D-EVRP, a hybrid rollout algorithm (HRA), which incorporates a dedicated pre-planning strategy and a rollout algorithm, is also proposed. The effectiveness of the HRA and benefits of incorporating the analytical battery model are justified by extensive simulations using the real-world D-EVRP instances.

关键词： Logistical planning electric vehicles (EVs) time-dependent stochastic traffic approximate dynamic programming

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Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

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NETWORKS 2019年第3期73卷 277-291页

作者： Ulmer, Marlin W. Tech Univ Carolo Wilhelmina Braunschweig Carl Friedrich Gauss Fak Braunschweig Germany

Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e-commerce and same-day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real-time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.

关键词： anticipation approximate dynamic programming degree of dynamism dynamic vehicle routing mixed-integer programming reoptimization stochastic requests

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