One of the problems associated with iterative learning control algorithms is the selection of a “proper” learning gain matrix for every discrete-time sample and for all successive iterations. This problem becomes more difficult in the presence of random disturbances such as measurement noise, reinitialization errors, and state disturbance. In this paper, the learning gain, for a selected learni...

This paper studies the use of Quantized State Control (QSC) in Time– Varying (TV) plants. Making use of a Lyapunov analysis, the stability properties of Time Invariant QSC are extended to the non– stationary case. Then, based on the resulting stability theorem, a design algorithm is developed. Finally, the use of this algorithm –which allows the design of QSC controllers according to stability ...