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optimal control of Logically Constrained Partially Observable and Multiagent Markov Decision Processes

IEEE TRANSACTIONS ON AUTOMATIC control 2025年第1期70卷 263-277页

作者： Kalagarla, Krishna C. Kartik, Dhruva Shen, Dongming Jain, Rahul Nayyar, Ashutosh Nuzzo, Pierluigi Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA Univ New Mexico Elect & Comp Engn Dept Albuquerque NM 87106 USA Amazon Seattle WA 98121 USA MIT Sloan Sch Management Cambridge MA USA

Autonomous systems often have logical constraints arising, for example, from safety, operational, or regulatory requirements. Such constraints can be expressed using temporal logic specifications. The system state is often partially observable. Moreover, it could encompass a team of multiple agents with a common objective but disparate information structures and constraints. In this article, we first introduce an optimal control theory for partially observable Markov decision processes with finite linear temporal logic constraints. We provide a structured methodology for synthesizing policies that maximize a cumulative reward while ensuring that the probability of satisfying a temporal logic constraint is sufficiently high. Our approach comes with guarantees on approximate reward optimality and constraint satisfaction. We then build on this approach to design an optimal control framework for logically constrained multiagent settings with information asymmetry. We illustrate the effectiveness of our approach by implementing it on several case studies.

关键词： Logic Planning Robots optimal control Markov decision processes Task analysis Stochastic processes Markov decision processes (MDPs) multiagent systems partially observable Markov decision processes (POMDPs) stochastic optimal control temporal logic

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optimal control for reachability of Markov jump switching Boolean control networks subject to output trackability

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INTERNATIONAL JOURNAL OF control 2025年第1期98卷 200-207页

作者： Li, Yalu Li, Haitao Xiao, Gaoxi Qingdao Univ Sch Math & Stat Qingdao 266071 Peoples R China Shandong Normal Univ Sch Math & Stat Jinan Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

This article aims at designing the finite horizon optimal control sequence (FHOCS) for ensuring the reachability of Markov jump switching Boolean control networks (MJSBCNs) subject to output trackability. The key step in solving such a problem is to construct a Luenberger-like observer (LLO). Our approach includes a few steps as follows. We firstly propose the formulation of the optimal control problem (OCP) of maximising the probability of finite-time reachability subject to output trackability. Then, by converting the dynamics of MJSBCNs into an equivalent expectation system, an LLO is established for MJSBCNs. In this setup, a Luenberger-like observation matrix is constructed, which is an effect tool to solve the finite-time reachability problem. Finally, an LLO method is developed to find the FHOCS by maximising the $ \mathbf {T} $ T-step Luenberger-like observation matrix. In a biological example, we show the effectiveness of our developed methods.

关键词： Markov jump switching Boolean control network Luenberger-like observer optimal control reachability output trackability semi-tensor product of matrices

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optimal control Allocation for Distributed Electric Propulsion in a Series/Parallel Partial Hybrid Powertrain

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JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME 2025年第1期147卷 011028页

作者： Litt, Jonathan S. NASA Glenn Res Ctr Intelligent Control & Auton Branch Cleveland OH 44135 USA

The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a National Aeronatics and Space Administration (NASA) concept transport aircraft representative of technology anticipated for a 2040 entry-into-service date. The powertrain consists of a single thrust-producing geared turbofan engine with generators driving a series/parallel partial hybrid power/propulsion system. The architecture includes 16 underwing contrarotating fans, eight on each side. The distributed fans can be used by the flight control system to augment or replace the rudder function. This paper sets up the optimal control problem of setpoint determination for individual wingfans in the distributed propulsion system, accounting for electrical string efficiencies, saturations, and failures. The solution minimizes power consumption while maintaining thrust and torque on the airframe for maneuvering. Additionally, thrust that would have been lost due to temporary fan speed or power saturation is optimally redistributed to maintain overall desired thrust and torque on the aircraft. A simulation of a coordinated turn utilizing the distributed electric propulsion for yaw rate control in a multiple wingfan failure scenario demonstrates the robustness of the powertrain design to failures and helps define its limitations.

关键词： electrified powertrain optimal control reconfigurable control

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optimal control of High-Risk Data Using the Convex Structured Method

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2025年 1-15页

作者： Zhu, Bolin Qiu, Dong Xie, Jialiang Guangxi Univ Ctr Appl Math Guangxi Sch Math & Informat Sci Nanning 530004 Guangxi Peoples R China Chongqing Univ Posts & Telecommun Coll Sci Nanan Chongqing Peoples R China Jimei Univ Coll Sci Xiamen Fujian Peoples R China

The formulation of policies for infectious disease prevention and control directly influences the subsequent development of the epidemic. However, recent research on infectious disease forecasting and control has been based primarily on deterministic mathematical models, and fixed model parameters increase the risk of epidemic prediction and control. To address this issue, we propose a convex structured parameter estimation method, an extension of the traditional interval parameter estimation method, in which the parameters are transformed into a convex fuzzy number to introduce uncertainty into the infectious disease model. Based on this method, this paper discusses in detail its application in epidemic control and policy formulation. By considering the risks of model parameter estimation and the costs of epidemic control, decision-makers can develop more aggressive and conservative practical strategies for epidemic prevention.

关键词： Convex structured method Basic reproduction number Convex fuzzy number optimal control Pareto optimal solutions

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optimal control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 96-109页

作者： You, Jin Li, Yan Cao, Xiangyang Zhao, Daduan Chen, Yangquan Zhang, Chenghui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Luoyang Inst Sci & Technol Sch Elect Engn & Automat Luoyang 471000 Peoples R China Univ Calif Merced Mechatron Embedded Syst & Automat Lab Merced CA 95343 USA

Modeling and control of epidemic-like spreading processes, such as contagions, rumors or products in complex networks, holds profound significance for understanding transmission mechanisms, formulating effective prevention and optimizing resource allocation. This paper investigates the optimal control problems for competing processes modeled by fractional-order bi-virus systems with memory in complex networks for the first time. The proposed model performs exceptionally well in capturing the dynamics of virus transmission and immune. First, the key indicators: bi-basic reproduction numbers (BBRNs) are introduced for the metapopulation model with nonlinear infectious functions in heterogenous networks. Then they are utilized to classify the equilibria, including extinction, absolute dominance and coexistence ones. Second, sufficient conditions for the stability of the equilibria are derived. Leveraging this result, an optimal therapeutic protocol is presented to eradicate viruses and minimize the cost function simultaneously. The corresponding optimal control solutions are generated via gradient descent algorithm, which can converge relatively quickly by iteratively adjusting control variables. Particularly, the role of fractional-order $\alpha$ in system modeling and optimization is deeply probed into. Finally, numerical examples are also included at the end of this paper to further shed light on the effectiveness of our results.

关键词： optimal control Epidemics Process control Predictive models Mathematical models Infectious diseases Prevention and mitigation COVID-19 Heuristic algorithms Heterogeneous networks Complex network competing process gradient descent algorithm fractional-order system optimal control

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optimal control strategies and cost-effectiveness analysis for infectious diseases under fractal-fractional derivative: a case study of Cholera outbreak

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2025年第3期71卷 4197-4226页

作者： Mohammadaliee, Behnam Samei, Mohammad Esmael Roomi, Vahid Rezapour, Shahram Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Bu Ali Sina Univ Fac Sci Dept Math Hamadan Iran Azarbaijan Shahid Madani Univ Dept Math Tabriz Iran Insurance Res Ctr Tehran Iran China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan

This paper presents a comprehensive analysis of an optimal control model for spread of infection in infectious diseases utilizing the Fractal-Fractional derivative with an exponentially decaying type kernel. The next-generation matrix method is employed to calculate the reproduction number. The equilibrium points of the model are calculated, and the local and global stability of the disease-free equilibrium point is also examined. Sensitivity analysis is discussed to assess the significance of the parameters. The existence and uniqueness of the solution for the model are demonstrated. A numerical method for modeling infectious diseases is introduced, applying the Newton polynomial. Also, optimal strategies to minimize the prevalence of the disease are identified. Through numerical simulations performed applying MatLab, the impact of various control measures, including disseminating health recommendations via the media, vaccination and therapy, on the spread of infection is analyzed. In addition, the effect of the fractional order and fractal dimension on the transmission dynamics of infection is investigated. By utilizing the incremental cost-effectiveness ratio, the most cost-effective and efficient optimal control strategy is determined. The model designed in this paper is used for transmission of infection in various infectious diseases. Finally, for example, the outbreak of Cholera in Yemen in 2017 is analyzed.

关键词： Fractal-fractional derivative optimal control Cost-effectiveness

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optimal control technique applied to the minimization of uncertainty measurements in surveying instruments

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MEASUREMENT & control 2025年第3期58卷 366-376页

作者： Feumo, Achille Germain Wounba, Jean Francois Talla, Andre Tueguem, S. Gervis Romeo Univ Yaounde I Dept Math box 510 yaounde Yaounde Cameroon Univ Yaounde I Dept Civil Engn Yaounde Cameroon Univ Yaounde I Dept Mech Yaounde Cameroon Univ Yaounde I Dept Phys Yaounde Cameroon

The objective of this study was to develop an optimal control approach by numerical calculus to predict how to reduce the overall uncertainty of survey instruments unable to directly measure inaccessible points. To reach our goal, two approaches were used to attain the objective. The first was inspired by mathematical models related to three methods appropriately selected and contained in Zhuo's work proposed in 2012. These were Remote Elevation Measurement (REM), Remote Elevation Dual Measurement (REDM), and Front-to-Back Measurement (FBM) methods whose uncertainties on the measurements of points were deduced using error propagation equations. optimal control technique helps us to show that for the REM, the height h of the prism contributed more than 70% compared to the global uncertainty for ranges S < 50 m from the prism. For the REDM, when the distance between two consecutive stations increases, the weight of the contribution of the two zenith angles z (1) and z (2) tends to 50% each for z (1) close to z (2) , which is to be avoided. For the FBM, the weight of the contribution during the front measurement process before is negligible. The second approach used the Swedish regulation of SIS-TS 21143:2009 which classified total stations according to types of uncertainty to compare the results given by the total station of class T3 unable to directly measure inaccessible points with the more sophisticated class T1 station with direct measurements. Thus, for small spans at the rear measurements S DG = 10 m , the height h 2 of the front prism has the greatest relative contribution more than 90% for zenithal differences z (1 )- z (2 )= 40 gon . This results of our analysis were convincing and provided designers with the data to minimize the overall uncertainties essential in the conception of total stations.

关键词： Total station inaccessible points uncertainty propagation of uncertainties optimal control

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optimal control of glucose-insulin dynamics via delay differential model with fractional-order

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ALEXANDRIA ENGINEERING JOURNAL 2025年 114卷 243-255页

作者： Rihan, Fathalla A. Udhayakumar, K. United Arab Emirates Univ Coll Sci Dept Math Sci Al Ain 15551 U Arab Emirates

This paper proposes a delay differential model with fractional order for glucose-insulin endocrine, metabolic regulation model, incorporating beta-cell dynamics to regulate and maintain bloodstream insulin concentration. In the model, two time delays are involved, namely delta(g) and delta(l), which represent delayed insulin secretion and delayed glucose reduction. A moderate hyperglycemia results in beta-cell growth (negative feedback), while a severe hyperglycemia results in beta-cell reduction (positive feedback). When a time delay passes a bifurcation point, Hopf bifurcation occurs. It is evident from biological findings that the model exhibits periodic oscillations. Furthermore, we present an optimal control problem for external insulin infusions to minimize prolonged high blood sugar levels. Numerical simulations have validated the theoretical results.

关键词： Delay differential equations optimal control Glucose-insulin Stability Chaos behavior Hopf bifurcation

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optimal control of unknown nonlinear systems via online parameter estimation with interval excitation

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NONLINEAR DYNAMICS 2025年第10期113卷 11779-11792页

作者： Luo, Rui Liu, Qiaoling Peng, Zhinan Shi, Kaibo Ghosh, Bijoy Kumar Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

This paper proposes an identifier-critic (IC) framework for optimal control of nonlinear systems with completely unknown dynamics. Unlike existing indirect adaptive/approximate dynamic programming algorithms, the developed IC framework consists of two adaptive neural networks (NNs): identifier and critic networks. The identifier networks are established to estimate the unknown dynamics, while the critic networks are applied to formulate the optimal control for an affine nonlinear system. To facilitate easy implementation and online computation, a linear regression method is introduced to establish the parameter estimation problem for the unknown weights in the NNs. Moreover, an interval excitation assumption, which is weaker than the persistence of excitation condition, is developed to ensure the convergence of the unknown weights in the IC framework within a finite time. The convergence of the weight estimation and the stability of the closed-loop system are theoretically demonstrated via Lyapunov theory. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed control method.

关键词： Identifier-critic neural network Linear regression equation Unknown nonlinear systems optimal control Relaxed persistence of excitation condition

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optimal control and global dynamics of a fractional-order infectious disease model incorporating vaccination and generalized incidence rate

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PHYSICA SCRIPTA 2025年第2期100卷 025242-025242页

作者： Xu, Conghui Yu, Yongguang Guo, Ke Ren, Guojian Lu, Zhenzhen Si, Xinhui Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Inner Mongolia Univ Sch Math Sci Hohhot 010021 Peoples R China

To investigate the transmission dynamics of infectious diseases driven by highly contagious pathogens, we propose a fractional-order model for infectious disease spread. The model encapsulates the isolation and vaccination measures by delineating the dynamics of isolated and vaccinated populations. The dynamic properties of the model are examined through the establishment of the basic reproduction number R0. By considering the value of R0 as a critical threshold, we analyze the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium within the proposed model. Ultimately, vaccination and isolation measures serve to diminish the number of effective contacts between infected and susceptible individuals, thereby leading to a reduction in the infection rate. Consequently, the control parameters are carefully chosen to modulate the infection rate, leading to the formulation of a corresponding fractional optimal control problem (FOCP). Utilize diverse datasets pertaining to the Corona Virus Disease 2019 (COVID-19) to identify the model parameters. The effectiveness of the proposed model in delineating the transmission dynamics of infectious diseases is corroborated through a comprehensive data fitting analysis. Concurrently, utilizing COVID-19 data, the associated theoretical results are numerically validated. Moreover, the FOCP is numerically addressed under various control strategies, offering theoretical insights for the control and prevention of infectious disease transmission.

关键词： Fractional-order infectious disease model optimal control stability parameter identification vaccination

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