Greenhouse Control With Computational Graphs and MBRL
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Solution Overview
Problem
Conventional greenhouse control systems in controlled agriculture environments are rule-based and lack self-learning capabilities, failing to capture the complexity of greenhouse dynamics, limiting their effectiveness in autonomous management.
Innovation Solution
A computer-implemented method and system using model-based reinforcement learning (MBRL) that simulates physical systems by forming computational graphs based on physical processes, integrating physics-based models and correction networks to iteratively adjust parameters for improved control inputs, enabling autonomous control of greenhouses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based control systems are used in greenhouses, then the system is simple to implement, but it cannot learn and improve itself, failing to capture the complexity of greenhouse dynamics
Solution Approach 1:
The system employs reinforcement learning agents that automatically learn optimal control strategies through interaction with the greenhouse environment, enabling the system to self-improve without external intervention. The agents continuously adjust control parameters based on observed outcomes, capturing the complexity of greenhouse dynamics while maintaining operational simplicity.
Solution Approach 2:
The patent transforms the static rule-based parameters into dynamic learning parameters that evolve through reinforcement learning. The control system adjusts parameters such as temperature setpoints, humidity levels, and irrigation schedules based on learned patterns from environmental data, allowing the system to adapt to complex greenhouse conditions.
2Reliability
If simplified rules are used for greenhouse control, then the system is easy to operate, but it does not capture the complexity of greenhouse dynamics
Solution Approach 1:
The patent replaces traditional mechanical rule-based control logic with intelligent software agents that use reinforcement learning. These agents process complex environmental data and generate optimized control decisions, substituting simple operational rules with adaptive algorithms that capture greenhouse dynamics complexity while maintaining ease of operation.
Solution Approach 2:
The reinforcement learning agents serve as intermediaries between environmental sensors and control actuators. They process complex environmental data from multiple sensors and translate it into optimized control commands for HVAC systems, irrigation, and lighting, bridging the gap between complex dynamics and simple control actions.
3Extent of automation
If conventional rule-based systems are used, then human intervention is required, but this increases operational costs and reduces automation
Solution Approach 1:
The reinforcement learning-based control system operates autonomously without requiring human intervention for decision-making. The agents continuously learn from environmental data and automatically adjust control parameters, achieving full automation while maintaining operational simplicity through automated routine management.
Solution Approach 2:
The system implements closed-loop feedback where sensors continuously monitor environmental conditions and control outcomes, and the reinforcement learning agents use this feedback to refine their control strategies. This automated feedback mechanism replaces manual monitoring and adjustment, achieving autonomous management while keeping the system easy to operate.
Data Source
AI summary
Various aspects related to methods, systems, and computer readable media for simulating and controlling a physical system, such as, for example, a greenhouse. A computer-implemented method can include forming a computational graph, wherein a structure of the computational graph is based on one or more physical processes in the physical system, receiving, from one or more sensors, measured values of one or more observed states of the physical system, setting initial values of one or more unobserved states of the physical system, receiving values of one or more control inputs a for the physical system, and iteratively simulating the physical system on a computer using x, y and a as simulation inputs to the computational graph.