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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2604.13461 |
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| _version_ | 1866911594638737408 |
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| author | Vakhnovskyi, Andrii |
| author_facet | Vakhnovskyi, Andrii |
| contents | Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross-coupling conflicts that waste 20-40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer-loop control variable. A 7-3-3 neural network optimizer selects energy-minimal temperature-humidity setpoints along the VPD constraint surface, feeding inner PID loops that drive HVAC actuators. Lyapunov stability analysis guarantees bounded PID gains. Deployment across 30+ commercial facilities in 8 U.S. climate zones over 7+ years demonstrates 30-38% HVAC energy reduction, 68-73% improvement in VPD stability, and 60-67% faster disturbance recovery compared to independent PID baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13461 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture Vakhnovskyi, Andrii Systems and Control Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross-coupling conflicts that waste 20-40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer-loop control variable. A 7-3-3 neural network optimizer selects energy-minimal temperature-humidity setpoints along the VPD constraint surface, feeding inner PID loops that drive HVAC actuators. Lyapunov stability analysis guarantees bounded PID gains. Deployment across 30+ commercial facilities in 8 U.S. climate zones over 7+ years demonstrates 30-38% HVAC energy reduction, 68-73% improvement in VPD stability, and 60-67% faster disturbance recovery compared to independent PID baselines. |
| title | VPD-Centric Cascading Control with Neural Network Optimization for Energy-Efficient Climate Management in Controlled Environment Agriculture |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.13461 |