This article studies the decentralized impulsive control problem for nonlinear interconnected systems (NISs) based on the dynamic event-triggered mechanism. By the fuzzy logic system (FLS)-based backstepping approach, we first propose a dynamic event-triggered impulsive controller. Unlike traditional event-triggered control (ETC), our impulsive control scheme allows for the instantaneous regulation of system states only at some state-dependent impulse instants, thus avoiding control inputs between two triggering moments. Notably, the resulting closed-loop impulsive systems include hybrid dynamics, general nonlinear characteristics, and prescribed performance constraints simultaneously. Based on the Lyapunov analysis method, we prove that even under discrete impulsive controllers, all closed-loop states remain bounded, and the prescribed tracking performance is achieved, namely, the tracking error can converge to a prescribed bounded region within a desired finite time. Then, the proposed impulsive control approach is further extended to the output-feedback case, under which the impulsive control design is based on the observer states. Finally, we show two simulation examples to validate the effectiveness of impulsive control schemes.

Rule-based static analysis tools are widely utilized for their high customizability. However, the creation of effective rules presents significant challenges, including the considerable human effort to handle the complexity of rules, and the additional costs involved in developing rules across various programming languages or frameworks. To address the significant challenges in manual rule creation for static analysis, this paper proposes a novel framework that leverages large language models (LLMs) to automate the generation of static analysis rules. The framework is specifically designed to alleviate the substantial human effort typically required in constructing and maintaining rule sets. Furthermore, we introduce a natural language-mediated rule migration methodology, which ensures semantic consistency when transferring functionally similar rules across different programming languages or frameworks. By seamlessly integrating LLMs with existing static analysis tools, our approach not only enhances the scalability and adaptability of rule generation but also enables efficient vulnerability scanning without the need for extensive computational resources such as large GPU clusters. This integration aims to bridge the gap between natural language understanding and program analysis, thereby facilitating more intelligent and resource-efficient static analysis. The method achieved 81.98 % grammatical and 74.73 % functional validity in rule generation for Semgrep, while migrating rules across 4 languages (Python, Java, JavaScript, and Golang). On the real-world engineering evaluation, it uncovered 9 unknown vulnerabilities in the latest version of the Linux Kernel undetectable by now. This work highlights the potential of our LLM-driven framework provides better handling of corner cases and maintains compatibility with industry-standard tools.

Epigenetics’ flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible. From the epigenetic perspective, the main limitations to improving the stability and accuracy of genetic algorithms are as follows: (1) the unchangeable nature of the external environment, which leads to excessive disorders in the changed phenotype after mutation and crossover; (2) the premature convergence due to the limited types of epigenetic operators. In this paper, a probabilistic environmental gradient-driven genetic algorithm (PEGA) considering epigenetic traits is proposed. To enhance the local convergence efficiency and acquire stable local search, a probabilistic environmental gradient (PEG) descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space. Moreover, to balance exploration and exploitation at different evolutionary stages, a variable nucleosome reorganization (VNR) operator is realized by dynamically adjusting the number of genes involved in mutation and crossover. Based on the above-mentioned operators, three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity. The experimental results on the open Congress on Evolutionary Computation-2017 (CEC’ 17) benchmark over 10-, 30-, 50-, and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance. The ablation analysis demonstrates that for accuracy and stability, the fusion strategy of PEG and VNR are effective on 96.55% of the test functions and can improve the indicators by up to four orders of magnitude. Furthermore, the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.

The flight reliability has been receiving considerable attention. However, the ability of the aircraft recovers to normal flight state from a perturbation were not considered under most circumstances. In this study, a simulation based intelligent analysis framework is proposed to identify the reliability, resilience and vulnerability states of Boeing 737 MAX aircraft disturbed by Maneuvering Characteristics Augmentation System (MCAS) system abnormal activation during the flight. Multiswarm particle swarm optimization (multiswarm PSO) algorithm based test cases generation strategy, aircraft failure behavior model which reflects aerodynamics of the aircraft after the horizontal stabilizer deflection caused by MCAS abnormal activation, JSBSim and FlightGear based co-simulation with aerodynamic and visual characteristics and neural network based flight states identification method constitute the proposed framework. Study results show that the proposed method can cover the margin of resilience and vulnerability quickly and the classification model can identify aircraft flight reliability, resilience and vulnerability states corresponding to different inputs accurately. The proposed framework can be used to validate the flight reliability and system resilience in a more efficient way.