＜正＞In this brief,based on Lyapunov-Krasovskii functional approach and appropriate integral inequality,some new...
XUE Mingxiang~(1,2),FEI Shumin~1,LI Tao~1,PAN Juntao~1 1.Key Laboratory of Measurement and Control of CSE(School of Automation,Southeast University),Ministry of Education,Nanjing 210096,Jiangsu,P.R.China 2.School of Mathematical Sciences,Anhui University,Hefei 230039,Anhui,P.R.China
This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. RBF neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.
In this paper, the absolute stability of Lurie control system with probabilistic time-varying delay is studied. By using a new extended Lyapunov-Krasovskii functional, an improved stability criterion based on LMIs is presented and its solvability heavily depends on the sizes of both the delay range and its derivatives, which has wider application fields than those present results. The efficiency and reduced conservatism of the presented results can be demonstrated by two numerical examples with giving some comparing results.
