rbfnn (Nonlinear Dynamics)
90
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Nonlinear Dynamics
rbfnn
Rbfnn, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rbfnns/rbf+neural+network/pmc11500898-51-12-5
Average 90 stars, based on 1 article reviews
Rbfnn, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rbfnns/rbf+neural+network/pmc11500898-51-12-5
Average 90 stars, based on 1 article reviews
rbfnn - by Bioz Stars,
2026-09
90/100 stars
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other:Article Title: Dual RBFNNs-Based Model-Free Adaptive Control With Aspen HYSYS Simulation Article Snippet: In this brief, we propose a new data-driven model-free adaptive control (MFAC) method with dual radial basis function neural networks (RBFNNs) for a class of discrete-time nonlinear systems.. The main novelty lies in that it provides a systematic design method for controller structure by the direct usage of I/O data, rather than using the first-principle model or offline identified plant model.. The controller structure is determined by equivalent-dynamic-linearization representation of the ideal nonlinear controller, and the controller parameters are tuned by the pseudogradient information extracted from the I/O data of the plant, which can deal with the unknown nonlinear system. Article Title: Consensus Tracking for High-Order Uncertain Nonlinear MASs via Adaptive Backstepping Approach Article Snippet: In this article, we focus on the problems of consensus control for nonlinear uncertain multiagent systems (MASs) with both unknown state delays and unknown external disturbances.. First, a nonlinear function approximator is proposed for the system uncertainties deriving from unknown nonlinearity for each agent according to adaptive radial basis function neural networks (RBFNNs).. By taking advantage of the Lyapunov– Krasovskii functionals (LKFs) approach, we develop a compensation control strategy to eliminate the effects of state delays. Control:Article Title: Solving dynamic encirclement for multi-ASV systems subjected to input saturation via time-varying formation control Article Snippet: Ocean Engineering 310 (2024) 118707 A 0 Contents lists available at ScienceDirect Ocean Engineering journal homepage: www.elsevier.com/locate/oceaneng Research paper Solving dynamic encirclement for multi-ASV systems subjected to input saturation via time-varying formation control Jiahui Zhang a, Yue Yang a,∗, Kezhong Liu a,b,c, Tieshan Li d a School of Navigation, Wuhan University of Technology, Wuhan 430063, China b Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China c National Engineering Research Center for Water Transport Safety, Wuhan 430063, China d School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China A R T I C L E I N F O Keywords: Formation control Marine vehicles Autonomous surface vehicle (ASV) Dynamic encirclement Input saturation A B S T R A C T Time-varying formation control design problems for dynamic encirclement of multiple autonomous surface vehicles (ASVs) are investigated in this paper.. Dynamic encirclement for multi-ASV systems is a strategy that can be used for continuous surveillance or to neutralize a target by limiting its movement.. In order to make the proposed control protocol more consistent with the actual environment of the multi-ASV system, each ASV is subjected to input saturation, internal uncertainties, and external disturbances. |