Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time
By Yongduan Song, Yujuan Wang, John Holloway, Miroslav Krstic. at - Automatisierungstechnik, 2017
1,092 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/557b9eb5492f68111d35860ef1f47a0e21b91123
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
In Control and Systems Engineering
Citations in the corpus
- Adaptive Fixed-Time Control for MIMO Nonlinear Systems With Asymmetric Output Constraints Using Universal Barrier Functions (2019, methodology): “More recently, instead of relying on the non-Lipschitz Lyapunov analysis as in the fixed-time control literature, some pioneering works [6]–[9] introduce the concept of prescribed finite-time control using time-dependent state”
- Adaptive Prescribed-Time Control for a Class of Uncertain Nonlinear Systems (2022, background): “, [25], [27], and [28]), where only the boundedness of the control input and the scaled state are guaranteed.”
- Prescribed-Time Control and Its Latest Developments (2022, methodology): “Remark 1: Assumption 2 is more general than the one used in [1] since the latter requires that b be known.”
- An Overview of Recent Advances in Fixed-Time Cooperative Control of Multiagent Systems (2018, background): “In [90], timevarying feedback control is proposed to stabilize normalform nonlinear systems in a prescribed finite time, which is actually an optimal control with a terminal constraint by employing time-varying gains.”
- Prescribed-Time Consensus and Containment Control of Networked Multiagent Systems (2019, positive): “2) We do not use the state transformation method as in [24], thus skillfully avoid using direct feedback of the state from each agent (direct state feedback might cause the state of each agent to converge to zero instead of reaching consensus around a nonzero value).”