Adaptive iterative learning reliable control for a class of non-linearly parameterised systems with unknown state delays and input saturation

An adaptive iterative learning reliable control (AILRC) strategy is developed in this study for a class of non-linearly parameterised systems subject to unknown time-varying state delays and input saturation as well as actuator faults. In regard to non-linearly parameterised uncertainties, not only the non-linearly parameterised controlled object, but also the non-linearly parameterised input distribution matrix is investigated in this technical note. Without the need for precise system parameters or analytically estimating bound on actuator faults variables, the novel data-driven AILRC is constructed by a non-linear feedback term and a robust term. The non-linear influence brought by actuator faults, input saturation and state delays can be compensated with the resultant algorithms. It is shown that the L-[0,T](2) convergence of single-input-single-output and multiple-input-multiple-output systems is proved through a new time-weighted Lyapunov-Krasovskii-like composite energy function. The validity of the proposed AILRC is further verified by simulation.