With the deepening of research on wireless power transfer (WPT) technology, there is an urgent need to improve the output power of WPT systems. Therefore, this paper proposes a power boosting method based on a multilevel inverter and dual-resonant network. The system adopts a five-level inverter whose output voltage contains rich fundamental and harmonic components. The transmitting side adopts a dual-resonant compensation network, and, through its parameter configuration, the system can be in a resonant state at both the fundamental and third harmonic frequencies. The receiving side adopts two receiving channels which can simultaneously receive power at both frequencies, thus boosting the output power. Firstly, the overall structure and operating principle of the system are introduced. Then, the relationship between the system output power and efficiency with the inverter modulation parameters is obtained and the strategy for selecting the optimal operating points of the inverter is designed by comprehensively considering the THD of the inverter output voltage and the efficiency of the system. Furthermore, the proposed system is simulated in a Matlab/Simulink platform. Finally, an experimental setup is created to verify the proposed method. The results show that, compared with the single-channel WPT system, the proposed method can enhance the output power from 160 W to 310 W with a boosting coefficient of nearly double and with the highest efficiency exceeding 92%.

Multi-load wireless power transfer systems generally require the configuration of multiple transmitting coils. Using traditional single-output inverters will increase the number of inverters, leading to increased system costs and complex structures. Therefore, this paper proposes a single-input multi-output inverter that can drive multiple transmitting coils simultaneously. Compared with traditional single-output inverters and existing multi-output inverters, the proposed inverter utilizes a topology improvement design and an efficient expansion method. By adding only one inductor, one capacitor, and a small number of power switches, it can generate multiple controllable and stable outputs while ensuring output gain, which is expected to simplify the system structure and improve system performance. This paper first introduces the topology evolution process and operating principle of the proposed inverter. Secondly, a mathematical model is established to analyze its operating characteristics, and its parameter design is carried out. Meanwhile, a comparison with existing multi-output inverters is conducted. Then, the resonant compensation networks are analyzed and selected to match the requirements of different loads. Finally, a simulation model of the proposed inverter is constructed, and an experimental setup is set up. The feasibility and superiority of the proposed inverter are verified through simulation and experiments.

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.

Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking, platoon coordination, and traffic signal timing optimization, where its data-driven characteristics enhance adaptability to dynamic environments. Finally, the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.

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.