Improved quantized iterative learning control in the presence of data dropouts

This paper focuses on solving the simultaneous data quantization and data dropouts problems in multi-input multi-output unknown nonlinear discrete systems, with an emphasis on their full tracking control. First, an innovative quantization model-free adaptive iterative learning control scheme is proposed based on the dynamic linearization model of the system. Using a uniform quantizer with an improved encoding-decoding mechanism to quantise the system output, modelling the data dropouts problem as a Bernoulli sequence, and employing intermittent updating to deal with the effects of the random data dropouts problem in the system output, the inherent errors due to data quantization and data dropouts are successfully eliminated, and it is verified that only a small amount of input/output data is required to achieve the tracking and controlling error convergence. Finally, the effectiveness of the method can be demonstrated through theoretical derivation and simulation experiments.