Uncertainty, if not explicitly accounted for in controller design, can significantly degrade the optimal control performance of greenhouse production systems. Scenario-based stochastic MPC (SMPC) addresses uncertainty by approximating its underlying probability distributions through sampling. However, SMPC rapidly becomes computationally intractable and can suffer from growing uncertainty with longer prediction horizons. Terminal costs and constraints ensure closed-loop performance of SMPC, but designing these for greenhouse systems is challenging since they rely on steady-state targets that often do not exist in greenhouse production systems. To overcome these challenges, this work introduces RL-SMPC, which uses reinforcement learning (RL) to learn a control policy that constructs both terminal region constraints and a terminal cost function. Additionally, this policy serves as a nonlinear feedback policy to attenuate uncertainty growth in the open-loop solution of scenario-based SMPC. RL-SMPC's closed-loop performance is compared against standalone RL, MPC, and scenario-based SMPC on a greenhouse lettuce model under parametric uncertainty. Simulation results showed that RL-SMPC outperformed MPC across all prediction horizons and surpassed SMPC for horizons shorter than five hours. Moreover, the results indicated that at equal online computational cost, RL-SMPC outperformed SMPC.

The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Control (MPC) to optimize economic benefits in autonomous greenhouses. Previous research has explored the use of RL and MPC for greenhouse control individually, or by using MPC as the function approximator for the RL agent. This study introduces the RL-Guided MPC framework, where a RL policy is trained and then used to construct a terminal cost and terminal region constraint for the MPC optimization problem. This approach leverages the ability to handle uncertainties of RL with MPC's online optimization to improve overall control performance. The RL-Guided MPC framework is compared with both MPC and RL via numerical simulations. Two scenarios are considered: a deterministic environment and an uncertain environment. Simulation results demonstrate that, in both environments, RL-Guided MPC outperforms both RL and MPC with shorter prediction horizons.

A novel solution to control an hydraulic excavator during grading tasks is proposed, featuring a Model Predictive Controller designed using Local Model Networks (LMNs), i.e. linear time-invariant dynamic models averaged by nonlinear static functions. The Local Linear Models Tree (LoLiMoT) algorithm is employed to derive an LMN from experimental data of a real excavator. Then, a nonlinear MPC law is designed and implemented on the excavator's embedded control system. To further improve the computational efficiency, a time-varying MPC law is designed as well, where the LMN is linearized in real-time around the current operating point. Experimental results, conducted with the excavator in real-world conditions, show the effectiveness of both approaches in achieving performance comparable to state-of-the-art solutions, while utilizing a more compact dataset and without the need of the hydraulic cylinders' pressure measurement.