Discrete-time learning control algorithm for a class of nonlinear systems

Applies a discrete-time learning algorithm to a class of discrete-time varying nonlinear system. The author investigates the robustness of the algorithm to state disturbance, measurement noise and reinitialization errors. Then, the author proves that the input and the state variables will always be bounded if certain conditions are met. Moreover, the author shows that the input error and state error will converge uniformly to zero in absence of all disturbances. A numerical example is added to illustrate the results.