Robust model predictive control for greenhouse temperature based on particle swarm optimization

Application of model predictive control (MPC) in horticultural practice requires detailed models. However, even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties. To enforce robustness during the controller design stage, this paper proposes a particle swarm optimization (PSO)-based robust MPC strategy for greenhouse temperature systems. The strategy is based on a nonlinear physical temperature affine model. The robust MPC technique requires online solution of a minimax optimal control problem, which optimizes the tradeoff between set point tracking and cost requirements reduction. The minimax optimization problem is reformulated to a nonlinear programming problem with constraints. PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied. The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables, higher control precision, and stronger robustness than the conventional MPC.