Design Adaptive Non-Linear PID Control Using Reinforcement Learning for Optimal Autonomous Greenhouse Microclimate Regulation

A greenhouse (GH) system is a multi-input/multi-output (MIMO), dynamic, and energy-intensive environment that requires precise control for achieving optimal plant growing while minimizing energy consumption. Energy consumed by a GH system has indirect effects on the overall profitability. Determining optimal setpoints for a GH environment is challenging for traditional proportional–integral–derivative (PID) controllers, particularly for MIMO systems to reduce their energy consumption. A hybrid approach combining reinforcement learning (RL) with a radial basis function neural network (RBFNN), called neuro-tuner optimization (NTO), is proposed to control the GH climate and maximize energy efficiency. Herein, RL was developed using Q-learning, a popular algorithm, exhibiting high performance with a root mean square error of 0.013 in the testing phase and a correlation coefficient of 1. To validate and improve the effectiveness of the proposed NTO system, it was compared with another optimal control strategy. The proposed NTO system showed good results and enhanced energy efficiency by 19.7% (average), whereas the optimal control strategy improved energy efficiency by 3.6% (average). These results demonstrate the ability of the proposed NTO system to handle non-linear dynamic systems and enhance their overall performance. Thus, the proposed NTO system met the study objectives by improving the PID performance of a dynamic system while maximizing its energy efficiency.