Intelligent Control of Parabolic Trough Collectors via Deep Reinforcement Learning

The effective control of parabolic trough collectors (PTCs) remains a significant challenge due to the inherent non-linearities of the system and the continuous impact of environmental disturbances. Although PTCs are a key technology for industrial process heat and large-scale electricity generation, classical control strategies often struggle to maintain optimal performance under fluctuating conditions. To address these limitations, this paper presents a novel reinforcement learning (RL)-based controller, designed specifically for solar thermal systems. The proposed RL agent is designed to learn directly from operational data, enabling it to adapt its control policy in real time to mitigate external disturbances. Experimental results demonstrate that the RL controller achieves a fast and well-damped closed-loop response, significantly outperforming traditional control benchmarks. Specifically, the RL controller is compared with a Proportional-Integral controller combined with a feedforward controller, and with a Model-based Predictive Controller. In all simulation-based comparisons, the RL controller outperforms the aforementioned controllers in terms of setpoint tracking or disturbance rejection. These results highlight the potential of machine learning to improve the operational reliability and efficiency of complex renewable energy systems.