Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems

This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. B...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on fuzzy systems Ročník 28; číslo 10; s. 2363 - 2374
Hlavní autori: Li, Yong-ming, Min, Xiao, Tong, Shaocheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive algorithms
Adaptive control
Adaptive fuzzy control
Adaptive systems
Approximation error
Backstepping
Dynamical systems
Feedback
Fuzzy control
Fuzzy logic
Fuzzy systems
inverse optimal control
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Optimal control
Optimization
strict-feedback
uncertain nonlinear systems
ISSN:1063-6706, 1941-0034
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2935693