Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (3)

Search Parameters:
Keywords = fuzzy flocking control

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
21 pages, 2719 KB  
Article
Fuzzy Flocking Control for Multi-Agents Trapped in Dynamic Equilibrium Under Multiple Obstacles
by Weibin Liang, Xiyan Sun, Yuanfa Ji, Xinyi Liu, Jianhui Wu and Zhongxi He
Machines 2025, 13(2), 119; https://doi.org/10.3390/machines13020119 - 4 Feb 2025
Viewed by 847
Abstract
The Olfati-Saber flocking (OSF) algorithm is widely used in multi-agent flocking control due to its simplicity and effectiveness. However, this algorithm is prone to trapping multi-agents in dynamic equilibrium under multiple obstacles, and dynamic equilibrium is a key technical issue that needs to [...] Read more.
The Olfati-Saber flocking (OSF) algorithm is widely used in multi-agent flocking control due to its simplicity and effectiveness. However, this algorithm is prone to trapping multi-agents in dynamic equilibrium under multiple obstacles, and dynamic equilibrium is a key technical issue that needs to be addressed in multi-agent flocking control. To overcome this problem, we propose a dynamic equilibrium judgment rule and design a fuzzy flocking control (FFC) algorithm. In this algorithm, the expected velocity is divided into fuzzy expected velocity and projected expected velocity. The fuzzy expected velocity is designed to make the agent escape from the dynamic equilibrium, and the projected expected velocity is designed to tow the agent, bypassing the obstacles. Meanwhile, the sensing radius of the agent is divided into four subregions, and a nonnegative subsection function is designed to adjust the attractive/repulsive potentials in these subregions. In addition, the virtual leader is designed to guide the agent in achieving group goal following. Finally, the experimental results show that multi-agents can escape from dynamic equilibrium and bypass obstacles at a faster velocity, and the minimum distance between them is consistently greater than the minimum safe distance under complex environments in the proposed algorithm. Full article
(This article belongs to the Section Automation and Control Systems)
Show Figures

Figure 1

23 pages, 5058 KB  
Article
Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation
by Lihua Li, Min Li, Yao Yu, Yuchen Jia, Zhengkai Qian and Zongkui Xie
Animals 2024, 14(21), 3055; https://doi.org/10.3390/ani14213055 - 23 Oct 2024
Cited by 2 | Viewed by 2565
Abstract
The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined [...] Read more.
The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined positive- and negative-pressure ventilation (CPNPV) mode, a dynamic temperature model is constructed. Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). First, based on the principles of energy and mass balance, and by decoupling the relationship between positive- and negative-pressure ventilation volumes, a dynamic temperature model for layer houses under CPNPV was established. Then, the PID parameters and the proportional relationship between positive- and negative-pressure ventilation were optimized through fuzzy rules, and a proportional exponential function was introduced to adjust the scaling of the universe, enabling fine-tuned control. Finally, a temperature control model for the layer house was built using Simulink. The results show that the coefficients of determination (R2) of the constructed dynamic temperature models are between 0.79 and 0.88, respectively, indicating high accuracy. The designed VFPID method outperformed traditional on–off control and improved control precision by 20–23.53% and 10.34–22.22% compared with PID control and fuzzy PID(FPID) control methods, respectively. This study provides new insights for the development of environmental control equipment and precise environmental regulation of layer houses. Full article
(This article belongs to the Section Animal System and Management)
Show Figures

Figure 1

27 pages, 6715 KB  
Article
Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm
by Zhenglin Tang, Qiang Zhao, Duc Truong Pham and Xuesong Zhang
Processes 2024, 12(9), 1979; https://doi.org/10.3390/pr12091979 - 13 Sep 2024
Cited by 4 | Viewed by 1158
Abstract
An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the [...] Read more.
An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
Show Figures

Figure 1

Back to TopTop