Robust learning controller for discrete-time systems

For precise tracking control of a class of discrete-time nonlinear control systems, an interative learning control law is proposed and the robustness of the learning control system is investigated. The authors derive a sufficient condition under which the output of a system converges to a desired output and show that the asymptotic errors for the control input and the corresponding output are bounded even in the presence of initial condition errors and disturbances