In this article, an adaptive iterative learning control scheme is presented for a class of nonlinear parametric strict-feedback systems with unknown state delays, aiming to achieve the point-wise tracking of desired trajectory in a finite interval. The appropriate Lyapunov-Krasovskii functions are established to compensate the influence of time-delay uncertainties on the control systems. As the main features, the proposed approach integrates the command filter into the backstepping procedure to avoid the differential explosion problem that may occur with the increase of system order, and introduces the hyperbolic tangent functions into the learning controller to handle the singularity problem thus maintaining the continuity of input signal. The results of theoretical analysis and numerical simulation demonstrate that the tracking errors at the entire period will converge to a compact set along the iteration axis. Compared with the existing works, the proposed control scheme is promising to manifest the better performance and practicability owing to the learning mechanism, the dynamic model, as well as the implementation of controller.

This paper investigates the problem of adaptive tracking control for quadcopter in the presence of nonlinear configuration uncertainties. It utilizes a real-time brain-inspired learning control (RBiLC) method to address the challenges posed by nonlinear time-varying uncertain instructions. To address the issue of flight control law reconfiguration caused by unknown changes in the fuselage configuration (e.g., propellers or motors), this paper introduces an online learning-evaluation-optimization reconstruction mechanism based on RBiLC. The proposed adaptive learning controller mitigates the need for extensive human resources and reduces the time required for flight controller design. The Lyapunov-Krasovskii function is introduced as a compensatory measure to address the impact of parameter uncertainty on system stability. Furthermore, this paper proposes a signed sinusoidal function perturbation estimate to guide the direction and magnitude throughout the online learning process. The approach conducts a theoretical stability analysis on a quadcopter vehicle considering uncertainties in UAV dynamics modeling. The results demonstrate that the proposed scheme achieves superior control and faster adaptation, enabling the system to ultimately converge to a compact set within a limited time domain. Finally, software-in-the-loop (SITL) simulations and flight verification results are presented to validate the proposed control strategy Note to Practitioners —In various UAV application scenarios, UAVs often necessitate multiple motor and size configurations to accommodate diverse mission load requirements. This paper examines the self-learning reconfiguration control of a nonlinear UAV controller, focusing on achieving stable control despite uncertain propeller configurations. The proposed self-learning control method is inspired by the self-learning mechanism observed in the human brain. Without any manual intervention, the traditional proportional-integral-derivative (PID) control serves as the benchmark and is intricately combined with an evolutionary strategy for iterative learning. The learning process is realized during flight without relying on a precise model through real-time evaluation, nonlinear command tracking response, and iterative reconstruction using a design-based symbolic sine function optimal controller. The practice has proved that when the UAV model cannot be identified in real-time, the UAV controller can be reconfigured in real-time through the model-free control method based on brain-inspired learning iterations to achieve stable control of the UAV. The notable feature of this paper is the establishment of the RBiLC framework, the completion of the theoretical proof of stability, and the real-world flight experiment on the quadcopter

This article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed.