Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Mei Liu et al.
Summary: In this article, a bicriteria weighted (BCW) scheme is proposed to address joint drift and minimize joint velocity infinity norm. The scheme adopts a novel repetitive motion index that can theoretically decouple joint error and position error. By transformation, the BCW scheme is converted into a time-varying quadratic programming (QP) problem, and a dynamic neural network (DNN) system with a new Fisher-Burmeister function is proposed to solve the resulting QP problem. The proposed DNN system is proven to be free of residual errors and has a suppression effect on noise.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Long Jin et al.
Summary: In this article, a coevolutionary neural solution (CNS) is proposed by combining a simplified neurodynamics (SND) model with the particle swarm optimization (PSO) algorithm. The CNS exhibits stability and noise tolerance capacity, which enables it to effectively optimize nonconvex problems in real-world scenarios.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Jingkun Yan et al.
Summary: This article studies the trajectory tracking problem of redundant manipulators and proposes a planning scheme solved by a recurrent neural network (RNN) model. The proposed scheme can efficiently and quickly track a given trajectory, considering joint limits.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Computer Science, Artificial Intelligence
Lin Xu et al.
Summary: This paper proposes a new approach for smooth path planning of a mobile robot using a new quartic Bezier transition curve and an improved particle swarm optimization (PSO) algorithm. The quartic Bezier transition curve is constructed to ensure G3 continuity at the joints of the path segments, while the PSO algorithm is used to optimize the smooth path planning problem. Simulation experiments demonstrate the effectiveness and superiority of the proposed approach.
Article
Computer Science, Artificial Intelligence
Ameya D. Jagtap et al.
Summary: Kronecker neural networks (KNNs) are a new type of neural networks with adaptive activation functions, utilizing the Kronecker product to construct wide networks with low parameters. Theoretical analysis and empirical examples show that KNNs have faster loss decay compared to feed-forward networks under certain conditions. Additionally, the proposed Rowdy activation function eliminates saturation regions and can be applied in various neural network architectures, demonstrating effectiveness in computational experiments.
Article
Automation & Control Systems
Qinglai Wei et al.
Summary: This article presents a novel event-triggered near-optimal control (ETNOC) method for discrete-time (DT) constrained nonlinear systems. The method involves converting the tracking control problem to the regulation problem using a tracking error system, and developing a novel triggering condition using the time-triggered optimal value function and control law. The method is proven to ensure asymptotic stability and has a predetermined upper bound for the real performance index.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Yimeng Qi et al.
Summary: This paper constructs a new recurrent neural dynamics model using control-theoretical techniques to tackle nonstationary quadratic programming problems, effectively breaking through the limitations of traditional models and demonstrating excellent convergence and robustness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Jialiang Fan et al.
Summary: This article proposes a new data-driven motion-force control scheme to address the problem of redundant manipulator control. The scheme uses a recurrent neural network to estimate the structure information and demonstrates excellent performance and practicality.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Yao Ding et al.
Summary: This paper proposes a novel multi-feature fusion network (MFGCN) for hyperspectral image (HSI) classification. It utilizes multi-scale GCN and multi-scale CNN to refine and extract pixel-wise spectral-spatial features of HSI, and introduces a 1D CNN to extract spectral features for superpixels. The complementary multi-scale features are fused through concatenate operation. Experimental results show that the proposed method outperforms competitive methods on three datasets.
Article
Computer Science, Artificial Intelligence
Sathishkumar Moorthy et al.
Summary: This paper investigates the distributed leader-following formation control problem for multiple nonholonomic wheeled mobile robots using a bioinspired neurodynamic approach. A distributed estimator is developed for each follower robot to estimate the leader's states. A formation tracking control law is proposed for each follower robot based on the estimated states of the leader. A bioinspired neurodynamics-based backstepping controller is designed to solve the impractical velocity jumps problem. The sufficient conditions for asymptotic stability of the multiple mobile robot system are derived using a Lyapunov function-based approach. Simulation results demonstrate the effectiveness of the proposed controllers.
Article
Automation & Control Systems
Long Jin et al.
Summary: This article discusses the issues related to solving time-varying nonlinear equations and the challenges faced by current methods, proposing an anti-noise discrete-time neural dynamics method to address these problems. The effectiveness and feasibility of this method are demonstrated through experiments, proving its ability to accurately recognize human lower limb motion intentions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Mei Liu et al.
Summary: This paper proposes a gradient-based differential kWTA (GD-kWTA) network for the k-winners-take-all operation. The performance of the proposed network is substantiated through numerical simulations and proofs. Additionally, the GD-kWTA network is used as a robust control scheme for multi-robot coordination, demonstrating its effectiveness and feasibility.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Automation & Control Systems
Long Jin et al.
Summary: The study introduces a saturation-allowed neural dynamics model for solving perturbed time-dependent linear equations with noise-tolerance. The proposed model has been shown to have global convergence with zero theoretical error, and performs well under various additive noise conditions through theoretical analysis and experimental validation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Dechao Chen et al.
Summary: This research proposes a novel neural-network based model MZNN to effectively address time-dependent nonlinear optimization problems with multiple types of constraints. By leveraging the Lagrange method, the problem is converted into a time-dependent equality system with exponential convergence property. Numerical examples and mobile robot control application further demonstrate the performance and effectiveness of MZNN.
Article
Computer Science, Artificial Intelligence
Hegui Zhu et al.
Summary: In this paper, a new nonlinear nonmonotonic activation function called Logish is proposed, demonstrating better performance in image classification tasks compared to other common activation functions.
Article
Computer Science, Artificial Intelligence
Shuqiao Wang et al.
Summary: The paper introduces a zeroing neurodynamics approach for solving multi-linear systems with di-tensors, proposing three specific models that converge in finite time and investigating activation functions needed for constructing these models. Theoretical analyses demonstrate the stability of the proposed approach and the convergence of the models to the theoretical solution in finite time.
Article
Computer Science, Artificial Intelligence
Mei Liu et al.
Summary: This paper proposes a new class of neural dynamics models for attitude tracking control of a flapping wing micro aerial vehicle, which accelerates the convergence speed of attitude tracking errors and makes errors converge to zero in a short time. Simulation results and theoretical analyses confirm the superiority and effectiveness of the proposed controllers.
Article
Computer Science, Artificial Intelligence
Bo Peng et al.
Summary: In this paper, a k-winners-take-all (k-WTA) neural network is designed and applied to a task assignment problem in a multi-robot competitive target tracking scenario. The proposed neural network features a single neuron and a non-hard-limiting activation function, which greatly simplifies the model structure and reduces the computation cost. The stability and convergence property of the neural network is theoretically analyzed, and simulations demonstrate its effectiveness in tracking targets moving at higher speeds than the tracking robots.
Article
Automation & Control Systems
Lin Wei et al.
Summary: This paper proposes a novel algorithm for solving future dynamic nonlinear optimization problems, which can estimate future unknown information and suppress noise during the solving process to improve accuracy.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Weibing Li et al.
Summary: A ZNN model with exponential convergence is designed for solving time-variant quadratic programming problems with equality and inequality constraints. The introduction of a predefined-time stabilizer enables the ZNN model to achieve predefined-time convergence for the first time. Validations demonstrate the effectiveness and superiority of the proposed ZNN model in terms of convergence performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Huiyan Lu et al.
Summary: This article introduces a novel joint-drift-free scheme synthesized by a projected zeroing neural network model, which successfully remedies the joint-drift problems of redundant robot manipulators in noisy environments through theoretical analysis and experimental validation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Qinglai Wei et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2020)
Article
Engineering, Civil
Fenghua Zhu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Yimeng Qi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2020)
Article
Automation & Control Systems
Jingwei Lu et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2020)
Article
Computer Science, Artificial Intelligence
Long Jin et al.
APPLIED SOFT COMPUTING
(2018)
Review
Automation & Control Systems
Yang Xing et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2018)
Article
Engineering, Electrical & Electronic
Yang Yang et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2017)
Article
Automation & Control Systems
Tomas Komrska et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2016)
Article
Computer Science, Artificial Intelligence
Dongsheng Guo et al.
APPLIED SOFT COMPUTING
(2014)
Article
Computer Science, Artificial Intelligence
Alireza Nazemi et al.
COGNITIVE COMPUTATION
(2014)
Article
Automation & Control Systems
Binghuang Cai et al.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2010)
Article
Automation & Control Systems
Yunong Zhang et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2009)
Article
Computer Science, Software Engineering
BT Chen et al.
MATHEMATICAL PROGRAMMING
(2000)