Towards Sustainable Greenhouses: Intelligent Temperature Regulation Using Optimal Controller

ABSTRACT Greenhouse systems have become an increasingly essential method of providing a controlled environment for producing high crop production, good quality, and efficient utilization of water in sustainable agriculture. The controlling of temperature within greenhouses, however, remains a very complex undertaking, mainly owing to nonlinear plant dynamics and external disturbances. The research proposes an intelligent climate control technique for sustainable greenhouses by using an optimally tuned PID controller. The PID controller's parameters are optimized through the Arctic Puffin Optimization algorithm (APO) so that the temperature regulation is adaptive and accurate. A modeling technique based on ARX is implemented to describe the greenhouse dynamics. MATLAB simulations are used to verify the performance of the method. The findings reveal that the optimized controller presents very fast rise time, zero overshoot, and almost zero steady-state error. Hence, the proposed method may be seen as a viable alternative for energy-efficient, reliable, and scalable automation of greenhouses. Further work will consider the integration of fuzzy logic or neural networks, implementation in real-time with IoT connectivity, and comparative evaluation with other metaheuristic algorithms such as BOA, ACO, FOA, and MVO.