Xuefang Li

Most existing adaptive iterative learning control (AILC) schemes, while effective for state tracking for repetitive systems, rely on full state measurement. Although observer‐based AILC methods can achieve output tracking, they are predominantly hampered by observers with asymptotic convergence, which conflict with the finite‐time operation of AILC. To overcome the limitations, this article proposes a novel finite‐time observer‐based AILC scheme for a class of nonlinear high‐order fully actuated (HOFA) systems with input disturbances. By leveraging the HOFA system framework, the proposed approach first converts the original nonlinear HOFA system into a linear closed‐loop system, which facilitates the construction of a linear finite‐time observer and furthermore develops a corresponding AILC law. Rigorous convergence analysis of the constructed observer and the proposed AILC algorithm is conducted. The effectiveness of the proposed AILC method is validated through two illustrative examples and comparative studies with existing algorithms.

The repetitiveness prerequisite of iterative learning control has always been the main obstacle to promoting its practical applications. In this article, a novel adaptive iterative learning reliable control scheme is proposed for the nonrepetitive systems with multiple iteration-varying parametric uncertainties, where actuator faults and state delays are considered simultaneously. During the design of the controller, the class- $k_{\infty } $ function is leveraged to dispose of the unmodeled lumps of systems through neural networks, and the transformation of control signals is established to compensate for the negative impact of the inefficient actuator. The technical features of our approach lie in an innovative parametric estimation mechanism that integrates the hyperbolic tangent function and an auxiliary sequence is presented to accommodate the nonrepetitive uncertainties, thus achieving the zero-error convergence of output. As the main merits, the proposed control scheme is promising to manifest better performance and practicality than the existing methods, owing to the weak assumptions on the system dynamics, the little prior knowledge of parametric uncertainties, and the strong learning ability of the controller.

This study investigates the high-order internal model (HOIM) based data-driven iterative learning control of HSRs subject to faded channels. Firstly, the nonlinear train dynamics are converted into an input/output data-based model by using a linearization approach. Then, the HOIM of the desired speed trajectories is introduced and the fading channel is used to model the unreliable transmission network. Next, the model free adaptive iterative learning control (MFAILC) strategy is implemented based on the train input and faded output information, and the theoretical convergence analysis of the speed error is carried out. Eventually, the validity of the MFAILC scheme is checked in simulation by applying the CRH-380 HSRs on a StarSim hardware-in-loop semi-physical platform.

To enhance the operational performance of permanent magnet synchronous motor (PMSM) drives under nonlinear load conditions and external disturbances, this paper proposes a novel adaptive iterative learning control (AILC) based for speed tracking strategy. The proposed method is able to improve tracking accuracy while effectively mitigating torque ripples. To address system uncertainties, the PMSM model is first reformulated into a parametric representation. Based on this reformulation, an AILC framework is developed, incorporating multiple learning-based adaptive laws designed to simultaneously estimate and compensate for nonlinear loads and external disturbances. Compared to existing approaches, the proposed AILC method demonstrates superior adaptability and robustness, leading to enhanced speed tracking performance and reduced torque ripple. Furthermore, the convergence and stability of the proposed control scheme are rigorously analyzed using the Lyapunov-like theory. Finally, extensive comparative simulations and real-time experimental results validate the effectiveness and practical feasibility of the proposed AILC strategy.

This work presents a novel adaptive iterative learning control (AILC) approach for a class of nonlinear nonsquare systems with unknown control gain matrices. The proposed strategy avoids explicitly incorporating the control gain matrices into the control algorithms and does not require them to be invertible, thereby significantly broadening the applicability of AILC. In the proposed AILC method, a newly designed virtual control gain matrix is introduced, enabling the transformation of the unknown control gain matrix into a form of norm-bounded system uncertainty while accounting for input saturation. Building on this reformulation, a structurally simple AILC scheme is developed, incorporating an input-dependent auxiliary system to mitigate the effects of input constraints. Moreover, the proposed method is extended to nonaffine nonlinear systems by integrating neural network techniques. The convergence of the proposed AILC laws is rigorously analyzed within the composite energy function (CEF) framework, and its effectiveness is demonstrated through implementation on a permanent magnet synchronous motor (PMSM) and a numerical example of a nonaffine system.