Adaptive iterative learning reliable control for a class of non‐linearly parameterised systems with unknown state delays and input saturation. Issue 17 (4th October 2016) - Ji, Honghai; Hou, Zhongsheng; Fan, Lingling; Lewis, Frank L.
Abstract : 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.