Suboptimal Learning Control for Nonlinearly Parametric Time-Delay Systems Under Alignment Condition

In order to achieve perfect trajectory-tracking performance over the entire time interval at a higher convergence speed, this paper proposes a suboptimal learning control scheme for a class of nonlinearly parametric time-delay systems under alignment condition. The controller is designed by integrating the robust learning control with the suboptimal control, in which, the control Lyapunov function and Sontag formula are employed in generating a suboptimal controller for the nominal system, while robust learning control mechanism is applied to deal with nonlinearly parametric time-delay uncertainties. As the iteration number increases, the system state can follow its reference signal over the full time interval. The proposed method extends some existing results. Numerical simulations demonstrate that our suboptimal iterative learning control scheme improves the convergence performance in comparison with traditional solutions.