Robust Accelerated Iterative Learning Control Design for Discrete-Time Systems with Input Saturation

    Received 22 October 2025; accepted 29 May 2026; published 02 October 2026

    2026, Vol. 22, no. 3, pp.  677-692

    Author(s): Pakshin P. V., Emelianova J. P., Emelianov M. A.

    This paper considers the iterative learning control design problem for discrete-time systems with uncertain parameters and input saturation. To accelerate convergence of the learning process, a combination of heavy-ball methods from optimization theory and the Lyapunov vector function method for repetitive processes is proposed. An example is given, including a comparison with known results.
    Keywords: iterative learning control, discrete-time system, repetitive processes, stability, convergence, uncertain parameters, saturation, heavy-ball method, Lyapunov vector function, linear matrix inequalities
    Citation: Pakshin P. V., Emelianova J. P., Emelianov M. A., Robust Accelerated Iterative Learning Control Design for Discrete-Time Systems with Input Saturation, Rus. J. Nonlin. Dyn., 2026, Vol. 22, no. 3, pp.  677-692
    DOI:10.20537/nd260904


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