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Neural Network for Distributed Collaboration of Multiple Manipulators With Switching Topologies: A Game-Theoretic Perspective

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3213-3221页

作者： Liu, Mei Mu, Wenxin Lv, Xin Sun, Zhongbo Jin, Long Lanzhou University School of Information Science and Engineering Lanzhou730000 China Changchun University of Technology Department of Control Engineering Changchun130000 China

Since distributed control strategies can effectively reduce the operating load of the central processor, they have become a prominent research direction in the field of controlling multiple manipulators. However, existing distributed approaches predominantly rely on fixed topologies, overlooking the dynamic nature of task requirements. To address this limitation, this article proposes a distributed collaboration scheme that incorporates switching topologies. The distributed control problem is further formulated as a game-theoretic framework involving multiple manipulators. A dynamic neural network solver is then developed to approximate the optimal Nash equilibrium strategy, with its convergence and stability proven through theoretical analysis. The effectiveness of the proposed scheme and solver is validated through a series of physical experiments. © 2013 IEEE.

关键词： Redundant manipulators

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Data-driven output regulation control for constrained linear systems

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Science China(information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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controllability of heterogeneous networked sampled-data systems

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Science China(information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Internet information Intelligent Recommendation System Under Deep Learning and Big Data

Internet Information Intelligent Recommendation System Under...

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作者： Yan, Kai Gao, Hao Department of Information and Control Engineering Shenyang Institute of Science and Technology Liaoning Shenyang110000 China

The explosive growth of information on the internet poses great challenges to users. In response to the above issues, this article studies the construction of an intelligent recommendation system for internet information using a combination of deep learning and big data methods. This article adopts deep learning methods to model user behavior data and combines big data analysis to provide personalized recommendation services for users. This article compared the information recommendation accuracy of two other intelligent recommendation systems through experiments. The system designed in this article achieved an accuracy of 92.5% in this regard, far superior to the other two. Experimental results have shown that the system designed in this article not only improves recommendation accuracy, but also enhances user satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Integral Sliding Mode control for Automated Vehicles under Coding-Decoding Mechanisms with Constrained Bit Rate

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IEEE Internet of Things Journal 2025年第11期12卷 16214-16226页

作者： Zhang, Mingming Ding, Derui Shen, Bo Ge, Xiaohua University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai200093 China Donghua University College of Information Science and Technology Shanghai201620 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate. The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the bit rate assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim's simulation instances. © 2014 IEEE.

关键词： Decoding

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MATM-SN: A Deep-Reinforcement-Learning Model for Ocean Sensing Task Allocation Based on Generative Social Network

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IEEE Internet of Things Journal 2025年第10期12卷 13477-13486页

作者： Guo, Shuai Xia, Menglei Xue, Huanqun Song, Yutong Zhang, Zhitian Liu, Chao Qingdao University of Technology College of Information and Control Engineering Qingdao266520 China Qingdao University of Technology College of Science Qingdao266520 China Ocean University of China Department of Computer Science and Technology Qingdao266100 China

Due to the complexity of ocean sensing tasks, buoy detection in traditional ocean observation methods has the disadvantages of high cost and insufficient real-time performance. Ocean mobile crowd sensing technology collects high-resolution data in real-time through shipborne sensors and has significant potential for future development in the ocean Internet of Things. In this article, we consider a cooperative vessel activity that performs sensing tasks, incentivizing multiple vessels through dynamic external task profits. To spontaneously incentivize the team to take part in tasks and maximize the total profit of the vessels, we propose a multiagent task allocation model (MATM-SN) based on deep reinforcement learning (DRL). This model uses a generative adversarial network to predict the trajectory of the vessels, and STGCN is used to extract synergistic features between vessels effectively. DRL training provides an optimal task allocation strategy for vessels. Finally, we compared four baselines and demonstrated that the MATM-SN model outperformed other baselines regarding task completion rate and total efficiency. © 2014 IEEE.

关键词： Deep reinforcement learning

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Formation control Design of Fixed-wing UAVs Based on Model Reference Adaptive control

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 43-52页

作者： Lantos, Bela Kimathi, Stephen Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Department of Electrical and Electronic Engineering Dedan Kimathi University of Technology P.O.B. 10143 Dedan Kimathi Nyeri10100 Kenya

Formation control is currently a popular field of research due to the increasing application areas of unmanned aerial vehicles. Of concern is the stability of unmanned aerial vehicles (UAVs) formation while tracking especially under the influence of disturbances, and model parameter uncertainty. Since formation of the UAV formation while tracking, especially flying involves a mutual cooperation, consensus-based techniques are more suited such that a group of UAVs are collectively coordinated to perform some tasks. In this paper, a leader–follower formation control of homogeneous fixed wing UAVs based on model reference adaptive control with an integral error control compensation is presented. Model reference adaptive control is powerful and effective for handling uncertain and varying dynamics. This proposed distributed composite control strategy with a directed graph topology, although model-dependent can be effective to suppress disturbances affecting the vehicles, while achieving consensus-based tracking of a reference path. The numerical simulation results given show the applicability of this proposed control strategy. © 2025 Budapest University of technology and Economics. All rights reserved.

关键词： Directed graphs

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Sliding mode active disturbance rejection control for uncalibrated visual servoing

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International Journal of Systems, control and Communications 2025年第2期16卷 133-151页

作者： Pu, Cuiping Wu, Yi Ren, Jie Ran, Chenyang College of Mechanical and Electrical Engineering Kunming University Kunming China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Automation Shanghai Jiao Tong University Shanghai China

A sliding mode control algorithm based on a linear extended state observer is proposed to address the multi-source uncertainty of uncalibrated visual servoing in robotic arms. Uncertainty, nonlinearity, coupling, external disturbances, etc. are considered as total disturbances. The linear expansion state observer is designed to observe the total disturbance and compensate for it. This enhances the system’s anti-interference ability. The outer loop adopts sliding mode control law as state feedback. It suppresses the observation errors of the observer and ensures the stability of the system. Sliding mode control based on linear extended observer does not depend on specific tasks and system configurations. It provides a unified design framework for solving the problem of uncalibrated visual servoing. Through numerical simulations of three different disturbance cases, it was compared with PI linear extended state observer and linear active disturbance rejection to verify its good dynamic performance and disturbance rejection performance. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Robotic arms

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