Point-to-point iterative learning control with quantised input signal and actuator faults

Abstract

This paper applies iterative learning control to point-to-point tracking problems with a general networked structure. The data is quantised and transmitted through restricted communication channels from the controller to the actuator. Combining a logarithmic quantizer with an encoding and decoding mechanism to quantise the input signals reduces the influence of the quantisation error. New design algorithms are developed with conditions for convergence of the tracking error and an extension to fault-tolerant performance under actuator failures. A numerical-based case study demonstrates the application of the new designs, which includes a comparison with another ILC law and the relative merits of the encoding and decoding schemes.

Keywords:

• Iterative learning control
• quantised input signal
• encoding-decoding mechanism
• optimal design
• fault-tolerant

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by 111 Project [grant number B23008], National Natural Science Foundation of China [grant number 61203092], National Science Centre in Poland [grant number 2020/39/B/ST7/01487], National Natural Science Foundation of China [grant number 62103293], Fundamental Research Funds for the Central Universities [grant number JUSRP51733B] and Natural Science Foundation of Jiangsu Province [grant number BK20210709].