System uncertainty can be handled in different ways within MPC. Robust MPC, as the name indicates, robustly accounts for the uncertainty, often resulting in conservative solutions. While Stochastic MPC yields efficient solutions, a small probability of constraint violation is permitted based on a predefined risk parameter.

In contrast to Robust MPC and Stochastic MPC, we propose an MPC method (CVPM-MPC), which minimizes the probability that a constraint is violated while also optimizing other control objectives. The proposed method is capable of dealing with changing uncertainty and does not require choosing a risk parameter. CVPM-MPC can be regarded as a link between Robust and Stochastic MPC.

One promising application for the CVPM-MPC method is in the field of autonomous driving. In this context, the constraint violation probability directly correlates to the risk of collision, making the need for a method that minimizes this probability crucial.

This research area focuses on enhancing the efficiency and productivity of vertical farming through optimal control strategies. Vertical farming offers year-round cultivation by maintaining optimal growing conditions, leading to faster crop maturity and higher yields than traditional farming. However, it faces challenges such as high energy consumption. The research aims to optimize the growth of different crops (e.g., wheat, tomatoes, etc.) by adjusting inputs like water, radiation, and temperature and by determining the optimal growth period duration to maximize yearly yields. By employing a nonlinear, discrete-time hybrid model, we seek to balance resource use, yield profit, and growth period, demonstrating the significant potential of control theory in improving vertical farming practices.