2021
DOI: 10.1109/tac.2021.3061645
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Prescribed-Time Stabilization of a Class of Nonlinear Systems by Linear Time-Varying Feedback

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Cited by 110 publications
(48 citation statements)
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“…To solve this problem, a novel design scheme was proposed. By using the idea of scalarization, 24,32,35 the prescribed‐time stabilization problem of the vector‐valued system is transformed into the stability analysis problem of a scalar‐valued differential equation. It has been shown that all of the closed‐loop trajectories convergence to the origin in prescribed time.…”
Section: Discussionmentioning
confidence: 99%
“…To solve this problem, a novel design scheme was proposed. By using the idea of scalarization, 24,32,35 the prescribed‐time stabilization problem of the vector‐valued system is transformed into the stability analysis problem of a scalar‐valued differential equation. It has been shown that all of the closed‐loop trajectories convergence to the origin in prescribed time.…”
Section: Discussionmentioning
confidence: 99%
“…such that the FTLOR problem can be achieved in finite-time T 0 by the bounded LTV state controller (18) where 𝛾(t) satisfies (40). Proof.…”
Section: The Second Approachmentioning
confidence: 99%
“…The function 𝛾(t) in Theorem 2 satisfies lim t↑T 0 𝛾(t) = +∞, which implies that the gain matrix in the bounded LTV controller of Theorem 2 approaches infinity at finite-time T 0 and is thus not well defined for all t ≥ T 0 . To avoid possible problems in implementation, we can replace (40) by 𝜆 min (P(𝛾 max )) , and the system state x(t) satisfies…”
Section: [ T Tmentioning
confidence: 99%
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“…In addition, by using temporal scale transformation, a triangularly stable controller is proposed for the perturbed system in [31], a dynamic highgain feedback algorithm is established for strict-feedback-like systems in [19], and some distributed algorithms are developed for multi-agent systems in [32]- [34]. Based upon parametric Lyapunov equation, a finite-time controller and a prescribedtime controller are studied for linear systems in [1] and [21], and then generalized to nonlinear systems in [35].…”
Section: Introductionmentioning
confidence: 99%