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Optimal Cooperative Controls for Multi-motor Driving System in Long-wall Shearer

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第9期22卷 2686-2698页

作者： Lv, Yongfeng Zhao, Jun Miao, Baixue Chang, Huimin Ren, Xuemei Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan Peoples R China Shandong Univ Sci & Technol Coll Transportat Qingdao 266590 Peoples R China Shanxi Univ Sch Math Sci Taiyuan 030006 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

The traditional coal mining machine uses a single-motor system, which will terminate when encountering a hard road header surface because of power limitations. The same problem exists in large radar servo systems and other applications of heavy industrial. To address this issue, this paper develops the multi-motor driving servo system for the coal mining machine, and designs the adaptive optimal torques for the cut-off gear and the multi-motor system. Firstly, the multi-motor driving system for the coal mining machine is modeled. The optimal performance functions of the cut-off gear and the driving motors are presented, and the Nash equilibrium among the optimal torques is defined. Then, based on the given performance functions, the adaptive optimal torques are found by approximate dynamic programming (ADP) technique, which can find the saddle point and optimize the coal mining machine performance. Moreover, the neural network (NN) weight convergence in the ADP structure is investigated. The stability of the multi-motor driven system with the proposed torques is proved. Finally, taking the coal mining machine as an example, the effectiveness of the performance optimization strategies of cut-off gear and multi-driving motors is verified.

关键词： approximate dynamic programming coal mining machine large inertia systems multi-motor driving system optimal control

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ADP- and rollout-based dynamic vehicle routing for pick-up service via budgeting capacity

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年第2期37卷 513-557页

作者： Wu, Yu Zeng, Bo Jian, Ming Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl Engn Lab Big Data Applicat Integrated Transpo Chengdu 611756 Sichuan Peoples R China Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

We address a dynamic parcel pick-up problem for a capacitated vehicle, which involves collecting customers' parcels and handling service cancellations and requests. In the existing studies, service cancellation and/or the reuse of the released capacity from cancellation have not been considered by state-based decisions (i.e., decision policy) with incorporating stochastic information into decision-making. Aimed at minimizing the expected total travel distance while maximizing vehicle capacity usage, we formulate the problem as a Markov decision process and develop an approximate dynamic programming method. First, we aggregate the post-decision state, consisting of multiple numerical and set components, into a two-dimensional numerical vector by budgeting the vehicle's capacity for future requests. This results in a decrease in the size of the state searching space from exponential to quadratic. Second, we use the aggregation vector to approximate the post-decision state and obtain an offline policy in a lookup-table representation through approximate Value Iteration. Third, we apply the post-decision rollout algorithm online with the offline policy as its base policy, resulting in an online rollout policy. To evaluate our approaches, we conduct two sets of computational experiments with two stepsize rules (i.e., constant stepsize rule and 1/n stepsize rule) together with a epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document}-heuristic exploration measure adopted in developing offline base policy. The first set benchmarks a dynamic programming-solvable optimal policy on a simplified model to evaluate the optimality degree of our solving method. The second set compares our rollout policy with both the counterpart base policy and a 2-Opt-based heuristic, verifi

关键词： Pick-up service approximate dynamic programming Rollout Capacity budget Stochastic request Service cancellation Service opportunity transfer

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Multi-period integrated production and delivery scheduling to enhance online orders fulfillment considering due dates

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年 1-33页

作者： Wang, Jie Yang, Peng Tsinghua Univ Inst Data & Informat Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Div Logist & Transportat Shenzhen 518055 Peoples R China Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

Online order fulfillment under limited capacity is essential for e-commerce when faced with demand fluctuations or surges. This paper investigates a multi-period integrated production and delivery scheduling problem for fulfilling online orders with due dates. We model the problem as the Markov decision process and develop an approximate dynamic programming (ADP) algorithm to minimize the weighted sum of average waiting time and order timeouts. The performance and robustness of the ADP strategy are validated through numerical experiments across various scenarios with differing order volumes and demand patterns. We find that for a fixed total volume of orders, different patterns of demand arrival influence the integrated production and delivery schedules, among which scenarios with a decreasing demand pattern over time are particularly challenging. Additionally, we discover that settings with flexible delivery departure times are more advantageous when demand exceeds the predefined production capacities of businesses, whereas fixed delivery departure times do not result in significant negative impacts when demand is relative low. Our findings suggest valuable insights for e-commerce operations regarding adaptable production and delivery schedules in response to varying demand patterns.

关键词： Online order fulfillment Integrated production-delivery scheduling approximate dynamic programming Markov decision process Non-stationary demand

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Lookahead scenario relaxation for dynamic time window assignment in service routing

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2025年 192卷

作者： Paradiso, Rosario Roberti, Roberto Ulmer, Marlin Vrije Univ Amsterdam Dept Operat Analyt Amsterdam Netherlands Univ Padua Dept Informat Engn Padua Italy Otto von Guericke Univ Chair Management Sci D-39106 Magdeburg Germany

We consider a problem where customers dynamically request next-day home service, e.g., repair or installments. Unlike attended home delivery, customers cannot select a time window (TW), the service provider assigns a next-day TW to each new customer if the customer can feasibly be inserted in the service route of the next day without violating the TWs of the existing customers. Otherwise, customer service will be postponed to another day (which is outside the scope of this work). The provider aims to serve many customers the next day for fast service and efficient operations. Thus, TWs have to be assigned to keep the flexibility of the fleet for future requests. For such anticipatory assignments, we propose a stochastic lookahead method that samples a set of future request scenarios, solves the corresponding team-orienteering problems with TWs, and uses the solutions to evaluate current TW assignment decisions. For real-time solutions to the team orienteering problem, we propose to approximate its optimal solution value with an upper bound. The bound is obtained by solving the linear relaxation of a set packing reformulation via column generation. We test our algorithm on Iowa City data and compare it to several benchmark policies. The results show that our method significantly increases customer service, and our relaxation is essential for effective decisions. We further show that our policy does not lead to observable discrimination against inconveniently located customers.

关键词： Next-day service dynamic time-window assignment Stochastic lookahead approximate dynamic programming Column generation

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The derivation of iterative convergence calculation for a nonlinear MIMO approximate dynamic programming approach

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APPLIED MATHEMATICS AND COMPUTATION 2013年第9期219卷 4495-4502页

作者： Huang, Zhijian Ma, Jie Huang, He Shanghai Jiao Tong Univ Ocean Engn State Key Lab Shanghai 200030 Peoples R China Shanghai Maritime Univ Merchant Marine Coll Shanghai 200135 Peoples R China

The standard approximate dynamic programming has only one action output. It's applied to single control variable system, such as inverted pendulum. For multi-input multi-output system, approximate dynamic programming needs a complex scheme. Few papers have derived its iterative convergence calculation, or the presented algorithm lacks rigorous mathematical basis. This paper fist researches matrix analysis foundation for the derivation of multi-input multi-output approximate dynamic programming. The research finds flaws in mathematics of a typical algorithm of its derivation. Hence, we promote approximate dynamic programming to multi-input multi-output form. The detailed iterative convergence calculation of it is derived. An experiment shows its effect. This algorithm is proved to be rigorous in mathematics and not complicated. It is effective for the iterative convergence calculation of multi-input multi-output approximate dynamic programming. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Adaptive critic designs approximate dynamic programming Iterative convergence calculation Neural network

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An Optimal approximate dynamic programming Algorithm for Concave, Scalar Storage Problems With Vector-Valued Controls

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2013年第12期58卷 2995-3010页

作者： Nascimento, Juliana Powell, Warren B. Kimberly Clark Inc BR-04551000 Sao Paulo Brazil Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

We prove convergence of an approximate dynamic programming algorithm for a class of high-dimensional stochastic control problems linked by a scalar storage device, given a technical condition. Our problem is motivated by the problem of optimizing energy flows for a power grid supported by grid-level storage. The problem is formulated as a stochastic, dynamic program, where we estimate the value of resources in storage using a piecewise linear value function approximation. Given the technical condition, we provide a rigorous convergence proof for an approximate dynamic programming algorithm, which can capture the presence of both the amount of energy held in storage as well as other exogenous variables. Our algorithm exploits the natural concavity of the problem to avoid any need for explicit exploration policies.

关键词： approximate dynamic programming resource allocation storage

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Joint optimization of opportunistic maintenance and speed control for continuous process manufacturing systems considering stochastic imperfect maintenance

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 198卷

作者： Chen, Zhaoxiang Chen, Zhen Zhou, Di Pan, Ershun Shanghai Jiao Tong Univ Dept Ind Engn & Management State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 119077 Singapore Donghua Univ Sch Mech Engn Shanghai 200051 Peoples R China

For continuous process manufacturing systems (CPMSs) where production cannot be stopped, opportunistic maintenance and speed control are the main means to improve production completion probability. However, existing studies on the joint optimization of these two means ignored the stochastic characteristic of imperfect maintenance, which increases the risk of unplanned downtime for CPMSs. Therefore, a novel joint optimization method of opportunistic maintenance and speed control under stochastic imperfect maintenance is proposed. The opportunity time window (OTW) is introduced to characterize the production-constrained maintenance "opportunities" between two production batches. Based on the states and the number of imperfect maintenance times of machines at the end of the last production batch, the proposed method requires determining the maintenance schedule during the OTW with limited maintenance resources and the speed level for the next batch. Moreover, a stochastic flow manufacturing network is established to evaluate the weighted production completion probability under stochastic imperfect maintenance and production speed with different weights. The joint optimization problem to maximize the weighted sum of production completion probability over a finite operation horizon is formulated as a Markov decision process (MDP). Then, a tailored deep learning and knowledge based approximate dynamic programming algorithm, which incorporates the structural property of MDP, is developed to solve this optimization problem. Finally, a case study of the hot rolling manufacturing system is conducted to validate that the proposed method can improve production efficiency and reduce the negative impact of stochastic imperfect maintenance on stability.

关键词： Continuous process manufacturing systems Opportunistic maintenance Speed control Stochastic imperfect maintenance approximate dynamic programming

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Data-driven optimal shared control of unmanned aerial vehicles

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NEUROCOMPUTING 2025年 622卷

作者： Tan, Junkai Xue, Shuangsi Guo, Zihang Li, Huan Cao, Hui Chen, Badong Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xi An Jiao Tong Univ Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intell Xian 710049 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Cooperation between humans and autonomy is a critical topic of unmanned aerial vehicle (UAV) control. How to co-pilot the UAV with human operator to achieve optimal performance presents a significant challenge. In this paper, we propose a novel data-driven optimal shared control method for UAV using the Koopman operators to predict the nonlinear dynamics of the UAVs. An original shared control mechanism is established to allocate the relationship between optimal and human control inputs. The model of the system is learned from human maneuver data via the Koopman operator approach, and the optimal controller is approximated online using reinforcement learning techniques. The Lyapunov theory analyzes the stability of the proposed method. Compared with offline RL methods, the proposed method can learn the optimal controller online without a precise UAV dynamics model from human maneuver data. The effectiveness of the proposed method is demonstrated by numerical and Human-in-the-loop (HiTL) simulation.

关键词： Shared control Reinforcement learning Optimal control Koopman operator Unmanned aerial vehicle approximate dynamic programming

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approximate Information States for Worst Case Control and Learning in Uncertain Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 127-142页

作者： Dave, Aditya Nishanth Venkatesh, S. Malikopoulos, Andreas A. Cornell Univ Sch Civil & Environm Engn Ithaca NY 14850 USA

In this article, we investigate discrete-time decision-making problems in uncertain systems with partially observed states. We consider a nonstochastic model, where uncontrolled disturbances acting on the system take values in bounded sets with unknown distributions. We present a general framework for decision-making in such problems by using the notion of the information state and approximate information state and introduce conditions to identify an uncertain variable that can be used to compute an optimal strategy through a dynamic program (DP). Next, we relax these conditions and define approximate information states that can be learned from output data without knowledge of system dynamics. We use approximate information states to formulate a DP that yields a strategy with a bounded performance loss. Finally, we illustrate the application of our results in control and reinforcement learning using numerical examples.

关键词： Costs Decision making Stochastic processes Reinforcement learning Aerospace electronics Vehicle dynamics Uncertain systems approximate dynamic programming robust reinforcement learning uncertain systems worst case control

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Sourcing strategies in supply risk management: An approximate dynamic programming approach

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COMPUTERS & OPERATIONS RESEARCH 2013年第5期40卷 1371-1382页

作者： Fang, Jiarui Zhao, Lei Fransoo, Jan C. Van Woensel, Tom Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Eindhoven Univ Technol Sch Ind Engn NL-5600 MB Eindhoven Netherlands

In recent years, supply chains have become increasingly globalized. As a consequence, the world's supply of all types of parts has become more susceptible to disruptions. Some of these disruptions are extreme and may have global implications. Our research is based on the supply risk management problem faced by a manufacturer. We model the problem as a dynamic program, design and implement approximate dynamic programming (ADP) algorithms to solve it, to overcome the well-known curses of dimensionality. Using numerical experiments, we compare the performance of different ADP algorithms. We then design a series of numerical experiments to study the performance of different sourcing strategies (single, dual, multiple, and contingent sourcing) under various settings, and to discover insights for supply risk management practice. The results show that, under a wide variety of settings, the addition of a third or more suppliers brings much less marginal benefits. Thus, managers can limit their options to a backup supplier (contingent sourcing) or an additional regular supplier (dual sourcing). Our results also show that, unless the backup supplier can supply with zero lead time, using dual sourcing appears to be preferable. Lastly, we demonstrate the capability of the proposed method in analyzing more complicated realistic supply chains. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Supply disruptions Recurrent risks Sourcing strategies approximate dynamic programming

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