A new method for assessing the health status of aerospace equipment based on a belief rule base with balanced accuracy and complexity

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 27, 2025

The health status of aerospace equipment directly affects the operational capability entire system. Belief rule base (BRB) is an effective method for assessing that combines expert knowledge and historical data. However, in actual assessment, data provided by experts only form basic framework model. Therefore, BRB model with joint optimization structure parameters (BRB-SPO) proposed to achieve a balance between model's accuracy complexity. First, complexity model, parameter backward stepwise selection (BSS) full factorial design (FFD) are introduced. BSS constructs optimal set, while FFD determines best values Subsequently, constructed deduced using evidential reasoning (ER) calculation procedure, other optimized projection covariance matrix adaptive evolution strategy (P-CMA-ES). Finally, practicality validated through two examples.

Язык: Английский

Colonial bacterial memetic algorithm and its application on a darts playing robot

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends by integrating Cultural Algorithms and co-evolutionary dynamics inspired bacterial group behavior. This combination of natural artificial elements results in a robust algorithm capable handling complex challenges robotics, such as constraints, multiple objectives, large search spaces, models, while delivering fast accurate solutions. incorporates features like multi-level clustering, dynamic gene selection, hierarchical population adaptive mechanisms, enabling efficient management task-specific parameters optimizing solution quality minimizing resource consumption. The algorithm's effectiveness is demonstrated through real-world application, achieving 100% success rate robot arm's ball-throwing task usually with significantly fewer iterations evaluations compared to other methods. was also evaluated using CEC-2017 benchmark suite, where it consistently outperformed state-of-the-art algorithms, superior outcomes 71% high-dimensional cases demonstrating up 80% reduction required evaluations. These highlight CBMA's efficiency, adaptability, suitability specialized tasks. Overall, exhibits exceptional performance both evaluations, effectively balancing exploration exploitation, representing significant advancement robotics.

Язык: Английский

Research on Parameter Tuning of Electro-Hydrostatic Actuator Position Sliding Mode Controller Based on Enhanced Dynamic Sand Cat Search Optimization Algorithm

Energies, Год журнала: 2025, Номер 18(8), С. 1888 - 1888

Опубликована: Апрель 8, 2025

This paper proposes an Enhanced Dynamic Sand Cat Search Optimization algorithm (EDSCSO) designed to address the high-order nonlinearities and strong coupling issues in parameter tuning of position sliding mode controller for electro-hydrostatic actuators (EHAs). Traditional swarm intelligence optimization algorithms often struggle with transition from global local search, which leads being trapped optima results lower computational efficiency. To overcome these challenges, EDSCSO introduces escape mechanism, a stochastic elite cooperative bootstrap strategy, multi-path differential perturbation strategy. These enhancements significantly increase diversity population, facilitate smooth avoid optimum traps, better balance exploration exploitation capabilities algorithm. Based on this algorithm, surface convergence rate parameters within are optimized. Simulation validations conducted combined platform MATLAB/Simulink AMESim demonstrate that PID optimized by achieves smaller steady-state tracking errors, exhibits greater robustness, offers enhanced efficiency compared other algorithms. study provides effective strategy improve control performance EHA controller.

Язык: Английский