Pub Date : 2025-01-01Epub Date: 2024-11-29DOI: 10.1016/j.arcontrol.2024.100983
Mohammadreza Doostmohammadian , Alireza Aghasi , Mohammad Pirani , Ehsan Nekouei , Houman Zarrabi , Reza Keypour , Apostolos I. Rikos , Karl H. Johansson
Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multi-agent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain.
{"title":"Survey of distributed algorithms for resource allocation over multi-agent systems","authors":"Mohammadreza Doostmohammadian , Alireza Aghasi , Mohammad Pirani , Ehsan Nekouei , Houman Zarrabi , Reza Keypour , Apostolos I. Rikos , Karl H. Johansson","doi":"10.1016/j.arcontrol.2024.100983","DOIUrl":"10.1016/j.arcontrol.2024.100983","url":null,"abstract":"<div><div>Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multi-agent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100983"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Permanent Magnet Synchronous Motors (PMSMs) are recognized for high efficiency, torque-to-inertia ratio, and robust properties, making them ideal for the rapid development of electric vehicles, robotics, and the aerospace industry. Recently, Deep Reinforcement Learning (DRL) algorithms have gained significant attention in the control domain due to their independence from plant models and advanced decision-making capabilities. These features make DRL highly suitable for addressing challenges in PMSM control such as load disturbances, speed tracking, and parameter variations. This review explores recent DRL techniques applied to PMSM speed, current, and torque control. Discrete and continuous algorithms, including Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), are examined in terms of their basic principles, practical implementations, and the benefits they provide in overcoming challenges in PMSM control. In addition, to demonstrate the efficiency of DRL, the review provides a summary and comparison of DRL applied to optimize classical control methods elaborated within various PMSM control strategies. Comparisons of DRL implementations in PMSM control are highlighted to validate their real-time applicability in experiments, and potential areas for future research and improvement are outlined.
{"title":"Implementation of deep reinforcement learning in permanent magnet synchronous motors control: A review","authors":"Larbi Assem Moulai , Fardila M. Zaihidee , Saad Mekhilef , Jing Rui Tang , Marizan Mubin","doi":"10.1016/j.arcontrol.2025.101014","DOIUrl":"10.1016/j.arcontrol.2025.101014","url":null,"abstract":"<div><div>Permanent Magnet Synchronous Motors (PMSMs) are recognized for high efficiency, torque-to-inertia ratio, and robust properties, making them ideal for the rapid development of electric vehicles, robotics, and the aerospace industry. Recently, Deep Reinforcement Learning (DRL) algorithms have gained significant attention in the control domain due to their independence from plant models and advanced decision-making capabilities. These features make DRL highly suitable for addressing challenges in PMSM control such as load disturbances, speed tracking, and parameter variations. This review explores recent DRL techniques applied to PMSM speed, current, and torque control. Discrete and continuous algorithms, including Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), are examined in terms of their basic principles, practical implementations, and the benefits they provide in overcoming challenges in PMSM control. In addition, to demonstrate the efficiency of DRL, the review provides a summary and comparison of DRL applied to optimize classical control methods elaborated within various PMSM control strategies. Comparisons of DRL implementations in PMSM control are highlighted to validate their real-time applicability in experiments, and potential areas for future research and improvement are outlined.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101014"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-10-07DOI: 10.1016/j.arcontrol.2025.101029
Julio R. Banga , Sebastian Sager
Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This comprehensive approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. The inferred principles can also be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.
{"title":"Generalized inverse optimal control and its application in biology","authors":"Julio R. Banga , Sebastian Sager","doi":"10.1016/j.arcontrol.2025.101029","DOIUrl":"10.1016/j.arcontrol.2025.101029","url":null,"abstract":"<div><div>Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This comprehensive approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. The inferred principles can also be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101029"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-10-10DOI: 10.1016/j.arcontrol.2025.101027
Nathan P. Lawrence , Philip D. Loewen , Michael G. Forbes , R. Bhushan Gopaluni , Ali Mesbah
Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robustness features of MPC. To this end, this paper presents a tutorial-style treatment of RL and MPC, treating them as alternative approaches to solving Markov decision processes. In our formulation, RL aims to learn a global value function through offline exploration in an uncertain environment, whereas MPC constructs a local value function through online optimization. This local–global perspective suggests new ways to design policies that combine robustness and goal-conditioned learning. Robustness is incorporated into the RL and MPC pipelines through a scenario-based approach. Goal-conditioned learning aims to alleviate the burden of engineering a reward function for RL. Combining the two leads to a single policy that unites a robust, high-level RL terminal value function with short-term, scenario-based MPC planning for reliable constraint satisfaction. This approach leverages the benefits of both RL and MPC, the effectiveness of which is demonstrated on classical control benchmarks.
{"title":"A view on learning robust goal-conditioned value functions: Interplay between RL and MPC","authors":"Nathan P. Lawrence , Philip D. Loewen , Michael G. Forbes , R. Bhushan Gopaluni , Ali Mesbah","doi":"10.1016/j.arcontrol.2025.101027","DOIUrl":"10.1016/j.arcontrol.2025.101027","url":null,"abstract":"<div><div>Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robustness features of MPC. To this end, this paper presents a tutorial-style treatment of RL and MPC, treating them as alternative approaches to solving Markov decision processes. In our formulation, RL aims to learn a <em>global</em> value function through offline exploration in an uncertain environment, whereas MPC constructs a <em>local</em> value function through online optimization. This local–global perspective suggests new ways to design policies that combine robustness and goal-conditioned learning. Robustness is incorporated into the RL and MPC pipelines through a scenario-based approach. Goal-conditioned learning aims to alleviate the burden of engineering a reward function for RL. Combining the two leads to a single policy that unites a robust, high-level RL terminal value function with short-term, scenario-based MPC planning for reliable constraint satisfaction. This approach leverages the benefits of both RL and MPC, the effectiveness of which is demonstrated on classical control benchmarks.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101027"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-04-07DOI: 10.1016/j.arcontrol.2025.100987
Leonardo Pedroso , Pedro Batista , W.P.M.H. (Maurice) Heemels
The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as ultra large-scale systems (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions.
{"title":"Distributed design of ultra large-scale control systems: Progress, Challenges, and Prospects","authors":"Leonardo Pedroso , Pedro Batista , W.P.M.H. (Maurice) Heemels","doi":"10.1016/j.arcontrol.2025.100987","DOIUrl":"10.1016/j.arcontrol.2025.100987","url":null,"abstract":"<div><div>The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as <em>ultra large-scale systems</em> (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100987"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-03-21DOI: 10.1016/j.arcontrol.2025.100988
Juan Ramón Camarillo-Peñaranda , Ana Carolina Cunha , Bruno W. França , Francis de Abreu Oliveira , Luan de Oliveira Senna
Designing a VSC-HVDC controller capable of effectively operating in most operational scenarios is challenging due to the highly complex dynamics, unexpected failures, and system uncertainty prevalent in a broad range of post-deployment situations. Furthermore, the stability of the closed-loop system stands as a crucial and undeniable aspect that necessitates special attention during the system development phase. From today’s point of view, this review provides a comprehensive overview of the literature on control technologies, their characteristics, and control options applicable to HVDC systems. This article discusses their applications, advantages, limitations, and recent developments within these techniques.
{"title":"A review on VSC-HVDC control schemes","authors":"Juan Ramón Camarillo-Peñaranda , Ana Carolina Cunha , Bruno W. França , Francis de Abreu Oliveira , Luan de Oliveira Senna","doi":"10.1016/j.arcontrol.2025.100988","DOIUrl":"10.1016/j.arcontrol.2025.100988","url":null,"abstract":"<div><div>Designing a VSC-HVDC controller capable of effectively operating in most operational scenarios is challenging due to the highly complex dynamics, unexpected failures, and system uncertainty prevalent in a broad range of post-deployment situations. Furthermore, the stability of the closed-loop system stands as a crucial and undeniable aspect that necessitates special attention during the system development phase. From today’s point of view, this review provides a comprehensive overview of the literature on control technologies, their characteristics, and control options applicable to HVDC systems. This article discusses their applications, advantages, limitations, and recent developments within these techniques.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100988"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-11-08DOI: 10.1016/j.arcontrol.2025.101034
Anna Scampicchio, Elena Arcari, Amon Lahr , Melanie N. Zeilinger
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression; (ii) taking into account the necessary approximations to obtain a tractable MPC formulation; (iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control.
{"title":"Gaussian processes for dynamics learning in model predictive control","authors":"Anna Scampicchio, Elena Arcari, Amon Lahr , Melanie N. Zeilinger","doi":"10.1016/j.arcontrol.2025.101034","DOIUrl":"10.1016/j.arcontrol.2025.101034","url":null,"abstract":"<div><div>Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression; (ii) taking into account the necessary approximations to obtain a tractable MPC formulation; (iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101034"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The filtering identification idea is an effective tool for handling the parameter identification of systems with colored noise. The hierarchical identification principle is an effective approach for addressing the identification of complex systems. For multivariable equation-error autoregressive moving-average-like (M-EEARMA-like) models with colored noise, which are also called multivariable controlled autoregressive autoregressive moving-average-like (M-CARARMA-like) models, this paper investigates and proposes the filtered hierarchical generalized extended stochastic gradient identification method, the filtered hierarchical multi-innovation generalized extended stochastic gradient identification method, the filtered hierarchical generalized extended recursive gradient identification method, the filtered hierarchical multi-innovation generalized extended recursive gradient identification method, the filtered hierarchical generalized extended least squares identification method, and the filtered hierarchical multi-innovation generalized extended least squares identification method by using the filtering identification idea and the hierarchical identification principle from available input–output data. These filtered hierarchical generalized extended identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noise.
{"title":"Hierarchical generalized extended parameter identification for multivariable equation-error ARMA-like systems by using the filtering identification idea","authors":"Feng Ding , Ling Xu , Xiao Zhang , Huan Xu , Yihong Zhou , Xiaoli Luan","doi":"10.1016/j.arcontrol.2025.100993","DOIUrl":"10.1016/j.arcontrol.2025.100993","url":null,"abstract":"<div><div>The filtering identification idea is an effective tool for handling the parameter identification of systems with colored noise. The hierarchical identification principle is an effective approach for addressing the identification of complex systems. For multivariable equation-error autoregressive moving-average-like (M-EEARMA-like) models with colored noise, which are also called multivariable controlled autoregressive autoregressive moving-average-like (M-CARARMA-like) models, this paper investigates and proposes the filtered hierarchical generalized extended stochastic gradient identification method, the filtered hierarchical multi-innovation generalized extended stochastic gradient identification method, the filtered hierarchical generalized extended recursive gradient identification method, the filtered hierarchical multi-innovation generalized extended recursive gradient identification method, the filtered hierarchical generalized extended least squares identification method, and the filtered hierarchical multi-innovation generalized extended least squares identification method by using the filtering identification idea and the hierarchical identification principle from available input–output data. These filtered hierarchical generalized extended identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noise.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 100993"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-09-29DOI: 10.1016/j.arcontrol.2025.101020
Angela Fontan , Patricia Eustachio Colombo , Rosemary Green , Karl H. Johansson
Global food systems are at the center of some of the most pressing modern societal challenges: They are significant contributors to a range of systemic issues, including health problems and chronic diseases, greenhouse gas emissions and general environmental degradation, and increasing financial burdens on healthcare and economies. Within these complex systems, final sustainable consumption, which refers to the adoption of diets that are both healthy and environmentally friendly, plays a critical role. Significant changes in contemporary dietary patterns are essential to address the rising burden of chronic diseases and public health outcomes and the escalating climate crisis. Achieving these shifts requires coordinated action from policymakers, consumers, and the scientific community in an effort to support the development, implementation, and evaluation of advertising and policy instruments that promote healthier and more sustainable dietary choices. However, driving changes in dietary behavior is a complex challenge, shaped by the interplay of heterogeneous influences, including biological, social, cultural, environmental, political, and economic factors, and further complicated by the difficulty of validating proposed approaches in ways that are both efficient and ethically sound. This vision paper presents the problem of promoting healthy and environmentally friendly diets and their implications for environmental sustainability. In particular, it discusses a systems approach based on social network dynamics and social interventions, illustrating recent findings that demonstrate the potential of influence strategies to drive dietary change. Finally, key scientific challenges and emerging research opportunities are highlighted.
{"title":"A systems perspective on promoting sustainable food systems","authors":"Angela Fontan , Patricia Eustachio Colombo , Rosemary Green , Karl H. Johansson","doi":"10.1016/j.arcontrol.2025.101020","DOIUrl":"10.1016/j.arcontrol.2025.101020","url":null,"abstract":"<div><div>Global food systems are at the center of some of the most pressing modern societal challenges: They are significant contributors to a range of systemic issues, including health problems and chronic diseases, greenhouse gas emissions and general environmental degradation, and increasing financial burdens on healthcare and economies. Within these complex systems, final sustainable consumption, which refers to the adoption of diets that are both healthy and environmentally friendly, plays a critical role. Significant changes in contemporary dietary patterns are essential to address the rising burden of chronic diseases and public health outcomes and the escalating climate crisis. Achieving these shifts requires coordinated action from policymakers, consumers, and the scientific community in an effort to support the development, implementation, and evaluation of advertising and policy instruments that promote healthier and more sustainable dietary choices. However, driving changes in dietary behavior is a complex challenge, shaped by the interplay of heterogeneous influences, including biological, social, cultural, environmental, political, and economic factors, and further complicated by the difficulty of validating proposed approaches in ways that are both efficient and ethically sound. This vision paper presents the problem of promoting healthy and environmentally friendly diets and their implications for environmental sustainability. In particular, it discusses a systems approach based on social network dynamics and social interventions, illustrating recent findings that demonstrate the potential of influence strategies to drive dietary change. Finally, key scientific challenges and emerging research opportunities are highlighted.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101020"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-10-04DOI: 10.1016/j.arcontrol.2025.101028
Alec Edwards , Andrea Peruffo , Alessandro Abate
An emerging branch of control theory specialises in certificate learning, concerning the specification of a desired (possibly complex) system behaviour for an autonomous or control model, which is then analytically verified by means of a function-based proof. However, the synthesis of controllers abiding by these complex requirements is in general a non-trivial task and may elude the most expert control engineers. This results in a need for automatic techniques that are able to design controllers and to analyse a wide range of elaborate specifications. In this paper, we provide a general framework to encode system specifications and define corresponding certificates, and we present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks to provide candidate control and certificate functions, whilst using SAT-modulo-theory (SMT)-solvers to offer a formal guarantee of correctness. We test our framework by developing a prototype software tool, and assess its efficacy at verification via control and certificate synthesis over a large and varied suite of benchmarks.
{"title":"A general framework for verification and control of dynamical models via certificate synthesis","authors":"Alec Edwards , Andrea Peruffo , Alessandro Abate","doi":"10.1016/j.arcontrol.2025.101028","DOIUrl":"10.1016/j.arcontrol.2025.101028","url":null,"abstract":"<div><div>An emerging branch of control theory specialises in <em>certificate learning</em>, concerning the specification of a desired (possibly complex) system behaviour for an autonomous or control model, which is then analytically verified by means of a function-based proof. However, the synthesis of controllers abiding by these complex requirements is in general a non-trivial task and may elude the most expert control engineers. This results in a need for automatic techniques that are able to design controllers and to analyse a wide range of elaborate specifications. In this paper, we provide a general framework to encode system specifications and define corresponding certificates, and we present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks to provide candidate control and certificate functions, whilst using SAT-modulo-theory (SMT)-solvers to offer a formal guarantee of correctness. We test our framework by developing a prototype software tool, and assess its efficacy at verification via control and certificate synthesis over a large and varied suite of benchmarks.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101028"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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