Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks

By F. I. C. L. Philip Chen, Guo-Xing Wen, Yanjun Liu, Fellow Ieee Fei-Yue Wang. IEEE Transactions on Neural Networks and Learning Systems, 2014

602 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/21d5534779e8054ee8ae6636cf655a237462061f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Wei He
2. Changyin Sun
3. Yanjun Liu
4. Zhao Yin
5. Dan Wang
6. Chun Lung Philip Chen
7. Jun Wang
8. Zhouhua Peng

In Control and Systems Engineering

1. Wei He
2. Yanjun Liu
3. Changyin Sun
4. Shaocheng Tong
5. Yiting Dong
6. Chun Lung Philip Chen
7. Jun Wang
8. Zhouhua Peng

Citations in the corpus

• Consensus in multi-agent systems: a review (2021, methodology): “A traditional adaptive control method for reaching consensus in the presence of uncertainties has been used in Cheng et al. (2008). Lengthy and involved computations, regression matrix analysis, and the assumption of linearity-in-parameters are some of the inherent drawbacks of traditional adaptive ”
• Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback (2017, background): “Under Assumption 1 in 2), there exists a positive definite matrix Q0 such that θ L0θ = e Q0e [48], [49].”
• Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint (2017, background): “[39] developed a novel adaptive consensus control approach for nonlinear multiagent time-delay systems using NNs.”
• Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems (2016, result): “2452217 consensus control, most of the research results were limited to first- or second-order multiagent systems [5]–[10].”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, methodology): “In [13], radial basis function neural networks are used to approximate the uncertain non-linear dynamics of a multiagent time delay system.”
• Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2016, background): “Recently, using adaptive neural network (NN) control design techniques, several interesting solutions have been presented for the nonlinear systems [11]–[17].”
• Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay Systems (2017, methodology): “Remark 4: In order to enhance the approximation accuracy, most existing approximator-based adaptive consensus control approaches require the neuron or fuzzy rule number large enough [27], [34] so that the online computational burden becomes very heavy.”
• Blockchain and Cryptocurrencies: Model, Techniques, and Applications (2018, background): “Blockchain uses a variety of consensus algorithms to guarantee the data consistency and the fault-tolerant ability of the shared ledger among distributed nodes [16], [17].”
• Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation (2016, methodology): “In [48], an adaptive NN control method for the nonlinear multiagent systems with time-delay is proposed.”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “Meanwhile, technique based on neural networks (NNs) has been confirmed to be an useful way in function approximation since the exact information of the system is nonrequired [22]–[25].”
• Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties (2020, background): “Distributed consensus control problem for nonlinear time-delay multiagent systems was investigated in [7] by using neural networks (NNs).”