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基于高阶神经网络的多模型非线性自适应脱控制,用于植物工厂的微气候环境
Yonggang Wang1, Ziqi Chen1, Yingchun Jiang2
1College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
使用高阶神经网络的新的非线性自适应脱控制策略显著改善了工厂工厂的温度和湿度控制. 这种智能农业方法提高了作物生产效率和资源利用.
科学领域:
- 智能农业智能农业
- 农业工程 农业工程
- 控制系统 控制系统
背景情况:
- 植物工厂对于智能农业的高效作物生产至关重要,依赖于精确的环境控制.
- 温度和湿度是影响植物工厂内作物产量和质量的关键因素.
- 植物工厂环境中的复杂,非线性和多扰动力学带来了重大的控制挑战.
研究的目的:
- 提出一种新的非线性自适应脱控制策略,用于工厂工厂的精确环境监管.
- 解决工厂动态模型中固有的复杂性和不确定性.
- 提高温度和湿度的控制性能,从而提高整体农业效率.
主要方法:
- 开发一种非线性自适应脱控制方法,结合具有开关功能的线性和非线性脱控制器.
- 使用高级神经网络来估计和补偿未成型的非线性系统术语.
- 应用基于一般化最小差异控制率的自适应算法来管理系统不确定性.
主要成果:
- 与传统方法相比,拟议的控制策略显示了温度控制的平均值和标准误差的大幅减少 (平均误差:0.1655与0.3615;标准误差:0.6665与0.8425).
- 在湿度控制方面观察到显著改善,平均值和标准误差显著降低 (0.0221对比0.1475和0.1541对比0.441).
- 该战略使得快速的设定值跟踪和加强控制性能,即使在复杂的环境条件下.
结论:
- 拟议的非线性自适应脱控制策略有效地管理了工厂中的复杂动态.
- 这种先进的控制方法显著提高了温度和湿度调节的准确性.
- 在工厂中加强环境控制,可以提高生产效率,提高产品质量,在智能农业中优化资源利用.
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