Adaptive Fuzzy 1-Bit Event-Triggered Control for Stochastic Nonlinear Systems

Wu, Ruitong; Yu, Kunting; Li, Yongming

doi:10.1109/icaci52617.2021.9435869

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…In contrast, fuzzy event-triggered control is mostly used for specific types of nonlinear stochastic systems and has a relatively narrower application scope, requiring customized designs for specific systems [25,26]. Furthermore, fuzzy event-triggered control relies on complex data transmission mechanisms, reducing data transmission through event-triggered mechanisms but still requiring the design of complex triggering conditions and communication strategies, which increases the difficulty of system implementation to some extent [27,28]. In conclusion, although finite-time adaptive event-triggered control based on fuzzy logic demonstrates unique advantages in handling complex nonlinear systems, its drawbacks in terms of design complexity, computational resource requirements, and flexibility in practical applications still need to be balanced and optimized in specific applications.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.

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