Design of Model Predictive Control and IoT for Experimental Dual Axis Solar Tracker System based on FPGA

Abstract

In this paper, the experimental design and investigation of a dual-axis sensor-based solar tracking system (DASTS) for a 300 W PV system is carried out based on a field programmable gate array (FPGA) as the heart of the system. A model predictive control (MPC) approach is proposed for precise control of the experimental DASTS system. Moreover, classical proportional integral and derivative (PID) and fractional order PID (FOPID) control methods were proposed in order to make a fair comparison with MPC and demonstrate the efficiency and distinctive performance of the proposed controller. Based on the practical numerical and graphical results of the performance of the different methods in DASTS, it was found that the MPC outperforms the other methods, as it showed a significant increase in the generation efficiency by 20.2% compared to the fixed solar system. A modern approach was also presented, which exploits the FPGA, which supports Internet of Things (IoT) technology, to improve the performance of solar energy tracking, by monitoring the system’s measurements and thus increasing the efficiency of power generation from the system. Applying the capabilities of IoT technology allows remote monitoring and control of DASTS, which helps in making the right decisions based on sensor readings. Ultimately, based on the experimental system's results, it is evident how well the suggested system performs in terms of improving energy harvesting efficiency when compared to fixed panels. It also increases the generated energy by 20.2% by utilizing MPC and IoT technology.