DC Motor Drive Powered by Solar Photovoltaic Energy: An FPGA-Based Active Disturbance Rejection Control Approach

Abstract

This paper presents an experimental platform for regulating the DC motor angular speed powered by photovoltaic cells. The experimental platform comprises an Eco Green Energy EGE-260P-60 solar panel, DC/DC SEPIC converter, DC bus, DC/DC buck converter, DC motor and Nexys 4 board with an Artix-7 100T FPGA. The DC/DC SEPIC converter is used for harvesting the maximum amount of energy from the PV cells using the perturb and observe algorithm to track the maximum power point. The DC/DC buck converter is used as the motor drive using the active disturbance rejection control to regulate the angular speed of the DC motor. In addition, the FPGA architecture design is presented using a hierarchical top-down methodology with the VHDL hardware description language and Xilinx System Generator tool. The software takes advantage of the FPGA’s concurrency to simultaneously evaluate the different processes, which is the main reason for choosing this digital device. Several tests were performed on the platform such as irradiance changes, DC bus variations, DC motor connection and load torque variations applied in the motor shaft. The results indicate that the maximum power is obtained from the photovoltaic cells, establishing the minimum operating conditions. In addition, the control approach estimates and cancels the effects of disturbances caused by variations in the environmental conditions, photovoltaic system, DC bus, and load changes in order to regulate DC motor speed.

Hierarchical Flatness-Based Control for Velocity Trajectory Tracking of the “DC/DC Boost Converter–DC Motor” System Powered by Renewable Energy

“…However, papers where the DC/DC Boost converter has been used as a driver for a DC motor [48]- [57] did not use a renewable energy power source. On the other hand, works where a renewable energy power source did was used for the DC/DC power converter-DC motor system were devoted to the Buck [34], [44], Sépic [64], Sépic and Cuk [66] topologies. Nevertheless, the dynamic associated with the renewable energy power source was neglected for control purposes.…”

“…Additionally, a neuronal control [28], neuro-adaptive backstepping controls [29], [30], and an adaptive neurofuzzy H-infinity control [31], were proposed by using the neural networks technique. Other controls for driving the angular velocity of the DC/DC Buck converter-DC motor system were based on disturbance rejection controllers with a GPI observer [32], [33], [34], resonant extended state observers [35], [36], exact tracking error dynamics passive output feedback control (ETEDPOF) [37], [38], fault detection by error-based global analytical redundancy relations [39], and output feedback discrete-time model predictive control [40].…”

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

“…Meanwhile, Sira-Ramírez and Oliver-Salazar in [5] studied the concepts of active disturbance rejection control and differential flatness in two combinations of the Buck converter with DC motors. Moreover, in recent years, several active disturbance rejection control schemes have been developed for governing Buck converter-driven motor systems, e.g., [6][7][8][9][10][11], while the study of controls based on differential flatness enabling solving the trajectory tracking task have been proposed in [12][13][14]. On the other hand, the applications of zero average dynamics of fixed point induction control techniques to control the speed of a permanent magnet DC motor with a Buck converter were detailed by Hoyos et al, in [15][16][17][18].…”

“…Hence, the three unknowns ω, ω, and ω (3) can be computed. This means that the control law in (3) can be obtained by mimicking (11) and replacing the unknown variable ω (4) by another variable, say µ. Likewise, notice that (11) represents the flatness-based model of the full-bridge Buck inverter-DC motor system.…”

“…Meanwhile, the control gains were obtained after the parameters a = 0.2, ζ = 10 and ω n = 1200 were introduced in (5). Lastly, the reference variables i * , υ * , i * a , and u * av were generated when ω * was replaced into ( 8)- (10), and (11), respectively. • Controller board and signal conditioning.…”