参数统计
稳健性(进化)
模型预测控制
数学优化
温室
人口
线性化
计算机科学
生产(经济)
控制理论(社会学)
非线性系统
控制(管理)
数学
经济
统计
生物化学
化学
人口学
物理
宏观经济学
量子力学
人工智能
社会学
生物
园艺
基因
作者
Jan Lorenz Svensen,Xiaodong Cheng,Sjoerd Boersma,Congcong Sun
标识
DOI:10.1016/j.compag.2023.108578
摘要
Greenhouse climate control is important to provide sufficient fresh food for the growing population in an economical and sustainable manner. However, the developed crop-climate models are generally complex with parametric uncertainties and far from describing the real system accurately, which affects adversely the control system’s performance. To improve optimality and guarantee robustness during the control process, we develop and implement a stochastic model predictive control (MPC) scheme for greenhouse production systems considering parametric uncertainties. By leveraging the advantages of model linearization, the proposed chance-constrained MPC method enables a more straightforward formulation of uncertainty constraints and computationally cheaper optimization in comparison to directly using the nonlinear model. Finally, the efficacy of the proposed approach is demonstrated on a greenhouse climate control case study.
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