Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation.

abstract

• This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Cybern

author list (cited authors)

• Shen, M., Wang, X., Zhu, S., Wu, Z., & Huang, T.

complete list of authors

• Shen, Mouquan||Wang, Xianming||Zhu, Song||Wu, Zhengguang||Huang, Tingwen

publication date

• February 2024

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Adaptive Dynamic Programming (adp)
• Control Systems
• Convergence
• Costs
• Data-driven Control (ddc)
• Dynamic Programming
• Event-triggered (et) Control
• Indexes
• Lyapunov Methods
• Nonlinear Systems

Digital Object Identifier (DOI)

• 10.1109/TCYB.2023.3337779

start page

• 1178

end page

• 1188

volume

• 54

issue

• 2

URL

• http://dx.doi.org/10.1109/tcyb.2023.3337779