Model Predictive Control of a Food Production Unit: A Case Study for Lettuce Production

TL;DR

This paper demonstrates the application of model predictive control (MPC) to optimize lettuce production in a plant factory, effectively managing constraints and resource limitations under varying environmental conditions.

Contribution

It develops a detailed model of a lettuce production unit and evaluates a model-based state tracking control approach for resource-efficient growth management.

Findings

Controller successfully tracks reference under changing weather conditions.

Constraints and resource limitations are effectively managed.

MPC approach improves operational efficiency.

Abstract

Plant factories with artificial light are widely researched for food production in a controlled environment. For such control tasks, models of the energy and resource exchange in the production unit as well as those of the plant's growth process may be used. To achieve minimal operation cost, optimal control strategies can be applied to the system, taking into account the availability of resources by control reference specification. A particular advantage of model predictive control (MPC) is the incorporation of constraints that comply with actuator limitations and general plant growth conditions. In this work, a model of a production unit is derived including a description of the relation between the actuators' electrical signals and the input values to the model. Furthermore, a preliminary model based state tracking control is evaluated for production unit containing Lettuce. It could be observed that the controller is capable to track the reference while satisfying the constraint under changing weather conditions and resource availability.