This paper presents an adaptive Kalman filter (AKF)-enhanced iterative learning control (ILC) scheme to improve trajectory tracking in non-repetitive time-varying systems (NTVSs), particularly in industrial applications. Unlike traditional ILC methods that assume fixed system dynamics, gradual parameter variations in NTVSs require adaptive approaches to address factors such as tool wear and sensor drift, which significantly affect tracking accuracy. By integrating AKF, the proposed method continuously estimates time-varying parameters and uncertainties in real time, thus improving the robustness and adaptability of trajectory tracking. Theoretical analysis is conducted to confirm the robust convergence and stability of the AKF-enhanced ILC scheme under uncertain and time-varying conditions. Experimental results demonstrate that the proposed approach significantly outperforms conventional ILC methods, ensuring precise and reliable tracking performance in dynamic industrial scenarios.

This study developed a frequency-domain fault-tolerant iterative learning control (ILC) strategy for spatially interconnected systems subject to external disturbances and actuator faults. An equivalent one-dimensional system model was established by performing model transformation and reformulation on the original spatially interconnected system. On this basis, the iterative learning control law was designed using two-dimensional system theory, and the closed-loop system was further characterized as an iterative process model. By applying the generalized Kalman–Yakubovich–Popov (KYP) lemma, it was demonstrated that within a finite frequency range, the resulting discrete repetitive process can guarantee both batch-wise stability and monotonic asymptotic convergence of the tracking error. Experimental results obtained from a precision collaborative machining platform verify the effectiveness of the proposed control method.

Repetitive contouring in precision manufacturing is commonly judged by tolerance-band compliance rather than exact coincidence with a nominal centerline, while actuator limits and high-curvature regions may cause localized tube violations. This paper develops a tolerance-aware spatial iterative learning control framework that embeds the tolerance specification directly into the learning process through an arc-length-domain tolerance tube and a bounded normal reference-shaping variable. An alternating projection strategy is introduced to update the feedforward input and B-spline shaping coefficients, thereby coupling contour-error reduction with input feasibility and tube-admissible reference adaptation. The projected updates are shown to preserve feasibility, and bounded-perturbation robustness is established for practical arc-length-domain uncertainties. Experiments on a precision laser-marking platform demonstrate that the proposed method outperforms PID control and fixed-reference Norm-Optimal ILC in terms of RMS normal contour error, violation rate, and tube-compliance performance.

Unsupervised person re-identification (Re-ID) has been proven very effective and it boosts the performance in learning representations from unlabeled data in the dataset. Most current methods have good accuracy, but there are two main problems. First, clustering often generates noisy labels. Second, features can change because of different camera styles. Noisy labels causes incorrect optimization, which reduces the accuracy of the model. The latter results in inaccurate prediction for samples within the same category that have been captured by different cameras. Despite the significant variations inherent in the vast source data, the principles of invariance and symmetry remain crucial for effective feature recognition. In this paper, we propose a method called Invariance Constraint Contrast Learning (ICCL) to address these two problems. Specifically, we introduce center invariance and instance invariance to reduce the effect of noisy samples. We also use camera invariance to handle feature changes caused by different cameras. Center invariance and instance invariance help decrease the impact of noise. Camera invariance improves the classification accuracy by using a camera-aware classification strategy. We test our method on three common large-scale Re-ID datasets. It clearly improves the accuracy of unsupervised person Re-ID. Specifically, our approach demonstrates its effectiveness by improving mAP by 3.5% on Market-1501, 1.3% on MSMT17 and 3.5% on CUHK03 over state-of-the-art methods.