Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation
Mouquan Shen, Xianming Wang, Song Zhu, Zheng‐Guang Wu, Tingwen Huang
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.
Topics & Concepts
Control theory (sociology)Dynamic programmingComputer scienceNonlinear systemConvergence (economics)Lyapunov functionAdaptive controlMathematical optimizationMathematicsControl (management)AlgorithmArtificial intelligencePhysicsQuantum mechanicsEconomic growthEconomicsAdaptive Dynamic Programming ControlIterative Learning Control SystemsAdvanced Control Systems Optimization