信息工程最新文献

This paper investigates distributed optimal output consensus control for hypersonic flight vehicle (HFV) swarm under the constraint that the output must remain within a safe range. We propose a distributed integrated protocol consisting of both control and optimization parts. In the optimization part, we design a time-varying set for projection to affect the transient process of the optimization trajectory. In the control part, we design a time-varying safety set and employ correspondingly a safety controller with feedback linearization and reference tracking. In this way, the control and optimization parts can be well coordinated so that both the optimity and safety of the HFVs are achieved. We establish the convergence and safety analysis of the closed-loop system by using the small gain theorem and constructing time-varying control barrier function (CBF).

Model Predictive Control (MPC) has emerged as one of the most widely adopted and effective approaches in autonomous driving systems. Conventional design methodology of MPC systems, however, often rely on static rule-based architectures and predetermined control strategies, limiting their flexibility and responsiveness to complex and dynamic traffic environments. To enhance the system’s understanding of driver intentions and improve strategy adaptability, this paper proposes a novel autonomous driving framework, ChatMPC, that integrates Natural Language Processing (NLP) with MPC. The framework employs a Transformer-based sentence embedding model, Sentence-BERT (SBERT), to parse driving intents embedded in natural language commands (e.g., “overtake,” “follow”), and dynamically updates the MPC controller’s objective functions and constraints. This enables the generation of personalized driving behaviors aligned with user preferences. Simulation experiments conducted on the Matlab platform show that ChatMPC completes the full cycle from instruction parsing to control optimization in an average of 15 seconds, with MPC prediction requiring an average of 13.5 ms and a worst-case time of 22.2 ms, well within the 50 ms real-time budget. In typical traffic scenarios, the system achieves high tracking accuracy, with a following error of 0.827% and overtaking error of 1.67%, validating its real-time performance and effectiveness.

This research addresses the diagnosis of broken rotor bar faults in three-phase induction motors, focusing on steady-state conditions under different load levels and fault severity. Although numerous techniques exist, there is still a significant gap in comprehensive comparative evaluations that rigorously assess the interaction between signal processing, feature selection, and pattern classifiers, particularly concerning their robustness to noise and multiple performance criteria. An experimental investigation was carried out with electrical current and mechanical vibration signals, several signal preprocessing techniques, two feature selection strategies, Correlation-Based Feature Selection (CFS) and Wrapper, and a wide range of pattern classifiers, Decision Tree (DT), Naive Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The performance of the configurations was quantified by a multicriteria indicator, complemented by a dedicated robustness assessment by introducing white noise into the input signals. The most significant results reveal that vibration signals exhibit superior diagnostic robustness compared to electrical current signals, especially under noisy conditions. Furthermore, Wrapper-based feature selection consistently outperforms CFS, and configurations combining Wrapper with DT or NB classifiers emerge as the most suitable for detecting and diagnosing broken bars. Furthermore, the Wrapper-DT configuration efficiently classified defects even with the inclusion of 40% noise. This work provides data-driven insights into robust configurations for broken bar diagnosis, guiding the development of more reliable predictive maintenance systems, emphasizing signal modality, robust feature selection, and real-time applications.