Journal of Automation and Intelligence最新文献_第3页

Adaptive optimal tracking control for underactuated surface vessels using extended state observer and reinforcement learning 基于扩展状态观测器和强化学习的欠驱动水面舰船自适应最优跟踪控制

Journal of Automation and Intelligence Pub Date : 2026-03-01 Epub Date: 2025-09-24 DOI: 10.1016/j.jai.2025.09.002

Yinkun Li , Yawen Zhou , Yufeng Zhou , Li Chai

{"title":"Adaptive optimal tracking control for underactuated surface vessels using extended state observer and reinforcement learning","authors":"Yinkun Li , Yawen Zhou , Yufeng Zhou , Li Chai","doi":"10.1016/j.jai.2025.09.002","DOIUrl":"10.1016/j.jai.2025.09.002","url":null,"abstract":"<div><div>This paper investigates the adaptive optimal tracking control (AOTC) for underactuated surface vessels (USVs). Compared to the majority of existing studies, the control strategy in this paper innovatively combines an extended state observer (ESO) with reinforcement learning (RL). The designed ESO has high estimation accuracy and robust disturbance rejection capabilities for the unmeasurable information for USVs. To obtain the AOTC, the actor–critic (AC) networks based on RL are constructed to solve the Hamilton–Jacobi–Bellman (HJB) equations. Due to the uncertainties, it is challenging to obtain the optimal controller by directly solving the HJB equations. To address this issue, this paper employs neural networks (NNs) to approximate the uncertainties and solves the optimal controller via AC-RL and ESO. In addition, the adaptive parameters of the optimal controller is trained in parallel with AC networks, which can ensure that the trained networks can further improve tracking performance. The boundedness of AOTC for USVs is shown by Lyapunov stability theorem. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 24-34"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GRA: Graph-based reward aggregation for cooperative multi-agent reinforcement learning 基于图的协同多智能体强化学习奖励聚合

Journal of Automation and Intelligence Pub Date : 2026-03-01 Epub Date: 2025-10-30 DOI: 10.1016/j.jai.2025.10.006

Jingcheng Tang , Peng Zhou , He Bai , Gangshan Jing

{"title":"GRA: Graph-based reward aggregation for cooperative multi-agent reinforcement learning","authors":"Jingcheng Tang , Peng Zhou , He Bai , Gangshan Jing","doi":"10.1016/j.jai.2025.10.006","DOIUrl":"10.1016/j.jai.2025.10.006","url":null,"abstract":"<div><div>Multi-agent reinforcement learning (MARL) has proven its effectiveness in cooperative multi-agent systems (MASs) but still faces issues on the curse of dimensionality and learning efficiency. The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem, which is yet to be fully exploited in existing algorithms. In this work, we recognize a learning graph characterizing the dependence between individual rewards and individual policies. Then we propose a graph-based reward aggregation (GRA) method, which utilizes the inherent coupling relationship among agents to eliminate redundant information. Specifically, GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function, making each agent learn a decentralized executable cooperation policy. In addition, we propose a variant of GRA, named GRA-decen, which achieves decentralized training and decentralized execution (DTDE) when each agent only has access to information of partial agents in the learning process. We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 46-56"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Essay on robust prescribed-time stabilization 鲁棒定时镇定

Journal of Automation and Intelligence Pub Date : 2026-03-01 Epub Date: 2025-10-06 DOI: 10.1016/j.jai.2025.09.004

Yury Orlov

引用次数: 0

Looking back at JAI’s 2025: A year of steady progress and distinguished achievements 回望JAI 2025：稳中求进、成绩斐然的一年

Journal of Automation and Intelligence Pub Date : 2026-03-01 Epub Date: 2026-03-27 DOI: 10.1016/j.jai.2026.03.002

Yongduan Song

引用次数: 0

Agentic AI: The age of reasoning—A review 人工智能：推理的时代——回顾

Journal of Automation and Intelligence Pub Date : 2026-03-01 Epub Date: 2025-08-28 DOI: 10.1016/j.jai.2025.08.003

Ume Nisa , Muhammad Shirazi , Mohamed Ali Saip , Muhammad Syafiq Mohd Pozi

{"title":"Agentic AI: The age of reasoning—A review","authors":"Ume Nisa , Muhammad Shirazi , Mohamed Ali Saip , Muhammad Syafiq Mohd Pozi","doi":"10.1016/j.jai.2025.08.003","DOIUrl":"10.1016/j.jai.2025.08.003","url":null,"abstract":"<div><div>Artificial intelligence has experienced a significant boom with the emergence of agentic AI, where autonomous agents are increasingly replacing human intervention, enabling systems to perceive, reason, and act independently to achieve specific goals. Despite its transformative potential, comprehensive information on agentic AI remains scarce in the literature. This paper provides the first comprehensive review of agentic AI, focusing on its evolution and three core aspects: patterns, types, and environments. The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents, driven by advancements in reinforcement learning, neural networks, and large language models (LLMs). Five key patterns: tool use, reflection, ReAct, planning, and multi-agent collaboration (MAC) define how agentic AI systems interact and process tasks. These systems are categorized into seven categories, each tailored for specific operational styles and autonomy in decision making. The environments in which these agents operate are classified as static, dynamic, fully observable, partially observable, deterministic, stochastic, single-agent, and multi-agent, emphasizing the impact of environmental complexity on agent behavior. Agentic AI has revolutionized systems through autonomous decision making and resource optimization, yet challenges persist in aligning AI with human values, ensuring adaptability, and addressing ethical constraints. Future research focuses on multidomain agents, human–AI collaboration, and self-improving systems. This work provides researchers, practitioners, and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 69-89"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy adaptive finite-time inverse optimal control for active suspension systems 主动悬架系统模糊自适应有限时间逆最优控制

Journal of Automation and Intelligence Pub Date : 2025-12-01 Epub Date: 2025-07-29 DOI: 10.1016/j.jai.2025.07.002

Zhenggang Chen , Wei Wu , Shaocheng Tong

引用次数: 0

Novel multi-agent action masked deep reinforcement learning for general industrial assembly lines balancing problems 针对一般工业装配线平衡问题的新型多智能体掩蔽深度强化学习

Journal of Automation and Intelligence Pub Date : 2025-12-01 Epub Date: 2025-07-22 DOI: 10.1016/j.jai.2025.07.001

Ali M. Ali, Luca Tirel, Hashim A. Hashim

{"title":"Novel multi-agent action masked deep reinforcement learning for general industrial assembly lines balancing problems","authors":"Ali M. Ali, Luca Tirel, Hashim A. Hashim","doi":"10.1016/j.jai.2025.07.001","DOIUrl":"10.1016/j.jai.2025.07.001","url":null,"abstract":"<div><div>Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards, prevent project constraint violations, and achieve cost-effective operations. While exact solutions to such challenges can be obtained through Integer Programming (IP), the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios. Heuristic methods, such as Genetic Algorithms, can also be applied, but they frequently produce suboptimal solutions in extensive cases. This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process (MDP), without imposing assumptions on the type of assembly line a notable distinction from most existing models. The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning (DRL) agents to optimize task and resource scheduling. To enhance the efficiency of agent training, the paper proposes two innovative tools. The first is an action-masking technique, which ensures the agent selects only feasible actions, thereby reducing training time. The second is a multi-agent approach, where each workstation is managed by an individual agent, as a result, the state and action spaces were reduced. A centralized training framework with decentralized execution is adopted, offering a scalable learning architecture for optimizing industrial assembly lines. This framework allows the agents to learn offline and subsequently provide real-time solutions during operations by leveraging a neural network that maps the current factory state to the optimal action. The effectiveness of the proposed scheme is validated through numerical simulations, demonstrating significantly faster convergence to the optimal solution compared to a comparable model-based approach.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"4 4","pages":"Pages 299-311"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tire wear aware trajectory tracking control for Multi-axle Swerve-drive Autonomous Mobile Robots 多轴转向驱动自主移动机器人轮胎磨损感知轨迹跟踪控制

Journal of Automation and Intelligence Pub Date : 2025-12-01 Epub Date: 2025-06-06 DOI: 10.1016/j.jai.2025.05.003

Tianxin Hu , Xinhang Xu , Thien-Minh Nguyen, Fen Liu, Shenghai Yuan, Lihua Xie

引用次数: 0

An adaptive dual-domain feature representation method for enhanced deep forgery detection 一种增强深度伪造检测的自适应双域特征表示方法

Journal of Automation and Intelligence Pub Date : 2025-12-01 Epub Date: 2025-11-07 DOI: 10.1016/j.jai.2025.11.003

Ming Li , Yan Qin , Heng Zhang , Zhiguo Shi

引用次数: 0

Review on the developments of sliding function and adaptive gain in sliding mode control 滑模控制中滑动函数和自适应增益的研究进展

Journal of Automation and Intelligence Pub Date : 2025-12-01 Epub Date: 2025-06-18 DOI: 10.1016/j.jai.2025.06.001

Ke Shao , Jinchuan Zheng , Minyue Fu

引用次数: 0