Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results

H∞H_{\infty} Robust Control Design for Teleoperation Systems

International audienceThis paper deals with the problem of delay-dependent robust $H_{\infty}$ control for time-varying delay teleoperation system with norm-bounded and time-varying model uncertainties. Thanks to our proposed control scheme, Lyapunov-Krasovskii functionals (LKF) and $H_{\infty}$ theory, the delay-dependent stability and tracking performance analysis are proposed in terms of Linear Matrix Inequality (LMI) optimization. An illustrative example is given by various simulations to prove that, our proposed solution is efficient to handle time-varying delays and uncertainties under different working conditions, such as abrupt tracking and wall contact motion