# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 2 Literature Reviews  
Several research studies have been conducted to develop algorithms for controlling greenhouses, as proposed in [PERSON] et al. [14]. One study proposed a simple self-tuning PID control method (STPID) to regulate temperature, humidity, and CO\({}_{2}\) concentration, using the Levenberg-Marquart algorithm to adjust the PID parameters as proposed by [PERSON] et al. in [3]. Another study employed a fuzzy logic control algorithm proposed by [PERSON] et al. in [4], where data from sensors were used as state variables, and factors such as heating, cooling, irrigation, lighting, and shading were generated as control variables to regulate the greenhouse environment. Rule tables created by experts were used to establish the relationship between state and control variables to produce control outputs on the basis of sensor data. In yet another study, an extended Kalman filter was applied together with a logic control algorithm to estimate the air temperature and humidity ratio instead of relying on the noisy sensor measurements proposed by [PERSON] [5]. This improved the efficiency of the greenhouse climate controller by reducing false alarms and fault detections. To enhance the performance of the logic-based control algorithm, [PERSON] et al. [6] adopted a genetic algorithm (GA) to adapt the controller parameters. Their proposed neuro-fuzzy controller used both temperature (T) and humidity (H) to provide smooth control, regardless of changing environmental conditions or plant complexity. However, these techniques required additional sensors to be installed inside the greenhouse for controlling purposes. [PERSON] et al. [7] developed a compound control scheme aimed at maximizing crop yield while minimizing energy consumption. The control laws of heating, fogging, and CO\({}_{2}\) injection were derived using feedback linearization techniques. A model-based fuzzy adaptive control approach was then applied to compensate for actuator saturation, and an energy-saving management mechanism was designed to minimize energy usage. Simulation results confirmed the effectiveness of this approach in energy conservation. Data-driven control algorithms have been applied to greenhouse scenarios as well. In a study by [PERSON] et al. [8], a deep multilayer perceptron (MLP) neural network was used as a controller to regulate the internal climate of the greenhouse. Simulation results indicated that the best performance was achieved with a neural controller consisting of two hidden layers, and an Elman neural model with two hidden and context layers. Similarly, an optimal neural-network-based nonlinear autoregressive model was proposed in [PERSON] et al. [9] to achieve desired growth conditions, such as temperature and humidity, for improved yield. In another study proposed by [PERSON] in [10], a fuzzy neural network was applied to regulate the internal climate of the greenhouse, and simulation results showed that this controller had certain improvements over conventional PID controllers in terms of overshoot, stability, and response time. Additionally, other machine-learning-based control strategies have been recently adopted for greenhouse control, including a deep reinforcement learning approach proposed by [PERSON] et al. in [15] for cucumber climate control, which controlled three controllable factors with a reward function aimedat improving cumulative fruit weight and reducing costs. However, data-driven control algorithms such as neural networks require large amounts of data to train models for effective greenhouse control. In another study proposed by [PERSON] et al. in [16], different control strategies, including PI control, fuzzy logic control, artificial neural network control, and adaptive neuro-fuzzy control, were compared to adjust greenhouse indoor temperature to the required value. In addition to traditional control approaches, such as PID and fuzzy logic, model predictive control (MPC) has recently been applied to greenhouse control, particularly for energy saving, as discussed in [PERSON] et al. [11].