Hierarchical Data-enabled predictive control

Abstract

With the rapidly growing world population and improving living standards of upcoming economies, the demand for fresh food is increasing vastly. Greenhouses have proven to be very effective in increasing crop yield since they offer a sheltered environment that can be controlled. Greenhouse growers need to take care of many processes, among which are maximising the crop yield while using as little resources as possible and controlling the indoor climate. An approach to alleviating demand for experienced growers is to automate processes within the greenhouse that aid in setpoint generation and setpoint tracking. Existing literature has presented multi-level architectures to cope with the two different timescales on which the two greenhouse subsystems act, i.e., the crop and greenhouse indoor climate subsystem. Current approaches in literature employ model-based techniques, which need tedious calibration
per considered situation due to the influences of the local weather, environment and highly non-linear underlying physical processes. Therefore, these model-based approaches are scarcely used in the horticultural sector. Thus, this thesis proposes a data-driven control scheme that deals with long-term crop production control while taking into account resource usage.

The proposed control scheme uses temporal and functional decomposition of the overall problem to accommodate the different timescales on which the greenhouse indoor climate subsystem and crop subsystem act. With the focus on the entire growing season, this thesis emphasises the importance of the climate strategy for crop production control. The aim of this thesis is threefold. Firstly, a non-linear simulator model for the crop dynamics has
been selected, adapted, and calibrated, using the Autonomous greenhouse challenge (AGC) dataset. Secondly, the overall crop production control problem has been decomposed into a hierarchical structure, including two subproblems with different objectives, acting on different timescales and using different system representations. The communication protocol between the two layers has been determined, and the setpoint tracking controller is established for the lower layer and tested on a benchmark temperature reference trajectory. Thirdly, the Data-enabled
predictive control (DeePC) algorithm is leveraged for the crop production control problem and compared in simulation against a conventional Economic model predictive control (EMPC) setpoint generating controller. Both use the aforementioned established setpoint tracking controller for controlling the greenhouse towards the generated climate strategies.

In simulation, it is shown that the DeePC setpoint generating controller results in a climate strategy that results in a lower crop yield by 1.51% when compared to the EMPC climate strategy and 0.54% less when compared to the predefined benchmark reference trajectory. Furthermore, the resource usage needed for the climate strategy proposed by the DeePC setpoint generating controller uses 1.75% more heating resources when compared to the EMPC setpoint generating controller and 11.1% more when compared to a predefined temperature setpoint trajectory. The DeePC climate strategy results in 7.92% and 5.27% less electricity use when compared to the EMPC and predefined benchmark climate strategy, respectively. The DeePC climate strategy CO2 usage is 11.4% less when compared to the EMPC climate strategy CO2 usage and 20.8% less when compared to the predefined benchmark climate strategy. The decrease in resource usage compensates for the lower crop yield of the DeePC climate
strategy. According to the metric of net economic profit, the DeePC generated climate strategy outperforms the benchmark climate strategy and EMPC climate strategy.