Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

This paper investigates an iterative learning control for single-input, single-output, and linear time-invariant discrete system. The special design of the learning gain matrix is introduced, where a finite uniform quantizer is incorporated with an encoding and decoding mechanism to realize the zero-error convergence of a tracking problem. Furthermore, the boundary-level calculation is considerably improved using lifting technique and infinity-norm of vectors under this mechanism. Some illustrations of the simulations verify the theoretical results.