Safe learning control for a class of uncertain nonlinear systems
LIU Yue-yue
WANG Hao-yu
WU Xiao-yu
FAN Qi-gao
Abstract：This paper addresses the safety control problem for nonlinear systems under nonparametric uncertainty con-ditions by proposing a control scheme based on Gaussian processes(GPs).Initially,leveraging historical data collected online,GP regression is employed to learn nonparametric uncertainty and time-varying disturbances within the nonlinear system.Subsequently,a feedback linearization control strategy is designed based on the Lyapunov theory,ensuring the con-troller's global uniform ultimate boundedness(GUUB).Secondly,considering the safety constraints,based on the feedback controller,control barrier function(CBF)is employed to minimize the control input,which obtains an optimal control input through quadratic programming(QP).Moreover,the boundedness of the closed-loop system and the forward invariance of the state safety domain are proved in a high-probability sense,respectively.Through simulation results,the effectiveness of the proposed control strategy in trajectory tracking and obstacle avoidance constraints under non-parametric uncertainty is verified.
Keywords：Gaussian process regressionfeedback linearizationcontrol barrier functionsafety learning controlquadratic program
Publication Date：2025-07-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1323-1332 )