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

• Published in: IET control theory & applications Vol. 10; no. 17; pp. 2160 - 2174
• Main Authors: Ji, Honghai, Hou, Zhongsheng, Fan, Lingling, Lewis, Frank L
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 21.11.2016
• Subjects: actuator faults variables; Actuators; adaptive control; Adaptive control systems; adaptive iterative learning reliable control; Control systems; convergence; data‐driven AILRC; Delay; delays; distribution matrix; fault tolerant control; Faults; feedback term; input saturation; iterative learning control; Learning; Lyapunov methods; matrix algebra; MIMO systems; multiple‐input‐multiple‐output systems; nonlinear control systems; Nonlinearity; nonlinearly parameterised uncertainties; Research Article; robust term; Saturation; single‐input–single‐output systems; time‐varying systems; time‐weighted Lyapunov‐Krasovskii‐like composite energy function; uncertain systems; unknown time‐varying state delays; time-varying systems; adaptive iterative learning reliable control; single-input–single-output systems; uncertain systems; convergence; data-driven AILRC; adaptive control; distribution matrix; unknown time-varying state delays; matrix algebra; actuator faults variables; MIMO systems; actuators; iterative learning control; feedback term; nonlinear control systems; delays; input saturation; robust term; time-weighted Lyapunov-Krasovskii-like composite energy function; nonlinearly parameterised uncertainties; multiple-input-multiple-output systems; fault tolerant control; Lyapunov methods
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2016.0209

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_{\lsqb 0\comma \, T\rsqb }^2 $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.