Closed-Loop Optimal Control of Greenhouse Cultivation Based on Two-Time-Scale Decomposition: A Simulation Study in Lhasa

Abstract

Due to the heavy computation load of closed-loop simulations, optimal control of greenhouse climate is usually simulated in an open-loop form to produce control strategies and profit indicators. Open-loop simulations assume the model, measurements, and predictions to be perfect, resulting in too-idealistic indicators. The method of two-time-scale decomposition reduces the computation load, thus facilitating the online implementation of optimal control algorithms. However, the computation time of nonlinear dynamic programming is seldom considered in closed-loop simulations. This paper develops a two-time-scale decomposed closed-loop optimal control algorithm that involves the computation time. The obtained simulation results are closer to reality since it considers the time delay in the implementation. With this algorithm, optimal control of Venlo greenhouse lettuce cultivation is investigated in Lhasa. Results show that compared with open-loop simulations, the corrections in yield and profit indicators can be up to 2.38 kg m−2 and 11.01 CNY m−2, respectively, through closed-loop simulations without considering the computation time. When involving the time delay caused by the computation time, further corrections in yield and profit indicators can be up to 0.1 kg m−2 and 0.87 CNY m−2, respectively. These conservative indicators help investors make wiser decisions before cultivation. Moreover, control inputs and greenhouse climate states are within their bounds most of the time during closed-loop simulations. This verifies that the developed algorithm can be implemented in real time.