Tracking systems with stringent performance are required to take advantage of solar energy produced through the use of High Concentration Photovolatics (HCPV) and Concentrating Solar Power (CSP) technologies. Closed-loop tracking systems use measurements provided by a sun sensor, which picks up direct and diffuse sunlight. High levels of diffuse sunlight produce increased levels of measurement noise at the output of the sensor, which in turn limits the maximal achievable gains in the closed-loop controller and consequently the tracking performance deteriorates. The goal of this work is to present the advantages of equipping a four-quadrant photodiode sun sensor with infrared and linear polarizing optical filters. Experimental results show that these filters reduce the effect of diffuse radiation and allow increasing the performance of sun tracking systems regardless of the type of controller used in the solar tracker. Tracking experiments are presented under indoor and outdoor conditions employing algorithms currently used in solar trackers including Proportional-Integral (PI), and Proportional-Integral-Derivative (PID) controllers, and a recently proposed cascade control law. In all the cases, the best performance is obtained with the sun sensor equipped with an infrared filter. Experiments show that an infrared optical filter reduces the Filtered Mean Squared Tracking Error up to 75under artificial light conditions and 90% under direct sunlight.

Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.