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IEEE Transactions on Cybernetics|November 8, 2021
Tracking Control of Self-Restructuring Systems: A Low-Complexity Neuroadaptive PID Approach With Guaranteed PerformanceQing Chen, Yujuan Wang, Yongduan Song
IEEE Transactions on Neural Networks and Learning Systems|January 24, 2017
Fraction Dynamic-Surface-Based Neuroadaptive Finite-Time Containment Control of Multiagent Systems in Nonaffine Pure-Feedback FormYujuan Wang, Yongduan Song
IEEE Transactions on Cybernetics|November 10, 2022
Prescribed-Time Tracking With Guaranteed Performance for a Class of Self-Switching Systems Under Unknown Control DirectionsYujuan Wang, Yongduan Song, Xiang Chen
IEEE Transactions on Neural Networks and Learning Systems|July 11, 2017
Distributed Adaptive Finite-Time Approach for Formation-Containment Control of Networked Nonlinear Systems Under Directed TopologyYujuan Wang, Yongduan Song, Wei Ren
IEEE Transactions on Neural Networks and Learning Systems|February 21, 2020
Neuroadaptive Fault-Tolerant Control Under Multiple Objective Constraints With Applications to Tire Production SystemsQian Cui, Yujuan Wang, Yongduan Song
IEEE Transactions on Cybernetics|January 24, 2017
Collectively Rotating Formation and Containment Deployment of Multiagent Systems: A Polar Coordinate-Based Finite Time ApproachYujuan Wang, Yongduan Song, Miroslav Krstic
IEEE Transactions on Cybernetics|November 22, 2019
Globally Exponentially Stable Tracking Control of Self-Restructuring Nonlinear SystemsYongduan Song, Liu He, Yujuan Wang
IEEE Transactions on Cybernetics|April 20, 2026
A Novel Approach for Adaptive Tracking Control of Nonlinear Systems Subject to Long-Range-Dependent Stochastic DisturbancesWufei Zhang, Yujuan Wang, Yongduan Song
IEEE Transactions on Cybernetics|March 21, 2019
Zero-Error Consensus Tracking With Preassignable Convergence for Nonaffine Multiagent SystemsYujuan Wang, Yongduan Song, David J Hill
IEEE Transactions on Cybernetics|October 18, 2022
Global Consensus Tracking Control for High-Order Nonlinear Multiagent Systems With Prescribed PerformanceZeqiang Li, Yujuan Wang, Yongduan Song, et al.
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