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Automation & Control Systems
Hui Ma, Qi Zhou, Hongyi Li, Renquan Lu
Summary: An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator with prescribed performance. The strategy effectively identifies unknown nonlinearity using a fuzzy-logic system and ensures transient performance of the control system. Dynamic signals are applied to handle unmodeled dynamics, while an event-triggered control law is developed to reduce communication load. The control method guarantees Lyapunov stability, backstepping technique, and semiglobal ultimately uniformly boundedness for all signals in the closed-loop system.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jinpeng Yu, Peng Shi, Jiapeng Liu, Chong Lin
Summary: This article discusses the problem of finite-time tracking control for a class of uncertain multi-input-multioutput nonlinear systems with input backlash. By designing a modified FT command filter, adopting an improved FT error compensation mechanism, and proposing a neural network adaptive technology, the desired tracking performance can be achieved in finite time.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zhou Gu, Choon Ki Ahn, Dong Yue, Xiangpeng Xie
Summary: This article focuses on the problem of resilient H-infinity filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach and a novel event-triggered mechanism are adopted, and the problem of denial-of-service attacks is also considered. Simulation results demonstrate the effectiveness of the theoretical analysis and design method.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jun Hu, Zidong Wang, Guo-Ping Liu
Summary: This article presents a delay-compensation-based state estimation method for DTVCNs with NIIOs and dynamical bias. A predictive scheme is proposed to compensate for communication delays, a new distributed state estimation approach is introduced, and performance evaluation criteria are proposed.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Lianbo Ma, Nan Li, Yinan Guo, Xingwei Wang, Shengxiang Yang, Min Huang, Hao Zhang
Summary: The article proposes an adaptive reference vector reinforcement learning approach for decomposition-based algorithms in industrial copper burdening optimization. The method utilizes reinforcement learning and reference point sampling operations to adapt reference vectors to problem characteristics and handle complex constraints. Experimental results confirm the competitiveness and effectiveness of the proposed algorithm in both benchmark problems and real-world instances.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jian Wu, Sha Wang, Francisco Chiclana, Enrique Herrera-Viedma
Summary: The study establishes a two-fold personalized feedback mechanism in social network group decision-making, which achieves a balance between group consensus and individual personality by generating trusted recommendation advice and personalized adoption coefficients.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Cheng He, Ran Cheng, Danial Yazdani
Summary: In large-scale multiobjective optimization, the proposed adaptive offspring generation method effectively generates promising candidate solutions, enhancing convergence and maintaining diversity.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Fuqing Zhao, Ru Ma, Ling Wang
Summary: The study introduces a self-learning discrete Jaya algorithm to address the energy-efficient distributed no-idle flow-shop scheduling problem in a heterogeneous factory system.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Wenhai Qi, Xianwen Gao, Choon Ki Ahn, Jinde Cao, Jun Cheng
Summary: The issue of sliding-mode control design for a class of nonlinear semi-Markovian switching systems via T-S fuzzy approach is studied. Novel integral sliding-mode surfaces and a fuzzy SMC law under complex stochastic semi-Markovian switching process are proposed. The theoretical findings are validated through an electric circuit model.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Guanhua Zhang, Minjing Yu, Yong-Jin Liu, Guozhen Zhao, Dan Zhang, Wenming Zheng
Summary: In this article, a sparse DGCNN model is proposed to improve the emotion recognition performance by imposing a sparseness constraint on the graph G. The research reveals that different brain regions may have different functions and the functional relations among electrodes are possibly highly localized and sparse. The experiments show that the sparse DGCNN model has consistently better accuracy than representative methods and has good scalability.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Automation & Control Systems
Juntao Fei, Yun Chen, Lunhaojie Liu, Yunmei Fang
Summary: This study proposes a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for nonlinear systems using terminal sliding-mode control (TSMC). The FDHLRNN shows advantages in approximation capability and control performance, and its effectiveness is verified through simulation examples and hardware experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Cybernetics
Sahar Vahdat
Summary: The COVID-19 pandemic has led to significant changes in HRM roles and highlighted the importance of technology in HR management. Companies and workers have had to rapidly adapt to new ways of working, presenting challenges for leaders worldwide.
Article
Computer Science, Artificial Intelligence
Yang Li, Wenming Zheng, Lei Wang, Yuan Zong, Zhen Cui
Summary: In this paper, a novel EEG emotion recognition method inspired by neuroscience is proposed. The method utilizes spatial and temporal neural network models to learn discriminative spatial-temporal EEG features. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in emotion recognition.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Automation & Control Systems
Guoqing Zhang, Jiqiang Li, Xu Jin, Cheng Liu
Summary: This article introduces a robust adaptive neural control algorithm for wing-sail-assisted vehicles, incorporating techniques such as multiport event triggering, sail force compensation, and neural network approximation. Through numerical simulations and practical experiments, the effectiveness of the algorithm has been confirmed.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Vladimir Stojanovic, Wojciech Paszke
Summary: This article proposes an optimal iterative learning control (ILC) algorithm for linear time-invariant multiple-input-multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The algorithm introduces the primal-dual interior point method to handle the input constraints, improving the constraint handling capability compared to conventional methods for nonuniform trial lengths. The algorithm also exhibits monotonic convergence property and its effectiveness is verified through numerical simulation of a mobile robot.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zhenhua Huang, Yajun Liu, Choujun Zhan, Chen Lin, Weiwei Cai, Yunwen Chen
Summary: This study introduces a novel group recommendation model with two-stage deep learning, which learns group preferences and optimizes recommendation performance through two sequential stages. Experimental results demonstrate that the proposed model outperforms existing models for group recommendation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Jian Wu, Xuemiao Chen, Qianjin Zhao, Jing Li, Zheng-Guang Wu
Summary: This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems. The unknown continuous functions are approximated using radial basis function neural networks (RBF NNs). The desired controller is designed using the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. The control performance is analyzed using stochastic Barbalat's lemma, and the tracking error converges to the accuracy defined a priori in probability under the constructed controller. The simulation results verify the availability of the presented control scheme.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Dongyu Li, Haoyong Yu, Keng Peng Tee, Yan Wu, Shuzhi Sam Ge, Tong Heng Lee
Summary: The study introduces a novel control problem called time-synchronized stability (TSS) with unique finite/fixed-time stability considerations, presenting sufficient conditions for achieving (fixed-) TSS. It is found that norm-normalized sign functions contribute to achieving TSS in control system design. Additionally, a fixed-time-synchronized sliding-mode controller for second-order systems is proposed and singularity avoidance is considered.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Pau Rodriguez, Guillem Cucurull, Jordi Gonzalez, Josep M. Gonfaus, Kamal Nasrollahi, Thomas B. Moeslund, F. Xavier Roca
Summary: This paper proposes an automatic system for pain assessment, which outperforms the latest techniques by feeding the raw frames to deep learning models and considering the temporal relation and whole image. The research achieves competitive results in the UNBC-McMaster Shoulder Pain Expression Archive Database and the Cohn Kanade+ facial expression database.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zhichao Sun, Hang Ren, Gary G. Yen, Tianfu Chen, Junjie Wu, Hongyang An, Jianyu Yang
Summary: In this article, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm (CRS-DE) is proposed to solve Highly Constrained Multiobjective Optimization Problems (HCMOPs). The algorithm relaxes the constraints by dividing the infeasible solutions into semifeasible subpopulation (SF) and infeasible subpopulation (IF), and devises corresponding reproduction and selection strategies for SF, IF, and feasible subpopulations. To prevent premature convergence, a mobility restriction mechanism is developed to restrict the individuals in SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
