The purpose of this paper is to introduce a new trajectory tracking controller applied to the Furuta pendulum; where the arm tracks a desired time-varying trajectory, while the pendulum remains regulated at the upward position. This controller is derived from the input-output feedback linearization technique. The rigorous analysis of the internal dynamics is presented, showing that the tracking error and the regulation error trajectories are uniformly ultimately bounded. Experimental results show the validity of the introduced theory. Additionally, a detailed experimental study is also presented, where a PID controller and an output tracking controller are compared with respect to the new algorithm, which presents the best performance.

This document proposes a parameter identification procedure, which overcomes drawbacks due to disturbances in an experimental platform. The main purpose of this work is to describe and formalize a matlab-based identification procedure that can be used by undergraduate and graduate students. The procedure can be easily extended to many types of system. As an application example, this work considers a two-degrees-of-freedom rigid link robot manipulator. The program code for matlab is provided, only requiring the joint position and applied torque measurements. Finally, the estimated parameters of the identified system are validated, showing that simulations and experiments are consistent. Assessment of the identification method by engineering students is described. Specifically, learning of parameter identification was observed since students were able to perform the proposed methodology and to apply it to other systems.

The COVID-19 pandemic has been the most critical public health issue in modern history due to its highly infectious and deathly potential, and the limited access to massive, low-cost, and reliable testing has significantly worsened the crisis. The recovery and the vaccination of millions of people against COVID-19 have made serological tests highly relevant to identify the presence and levels of SARS-CoV-2 antibodies. Due to its advantages, microfluidic-based technologies represent an attractive alternative to the conventional testing methodologies used for these purposes. In this work, we described the development of an automated ELISA on-chip capable of detecting anti-SARS-CoV-2 antibodies in serum samples from COVID-19 patients and vaccinated individuals. The colorimetric reactions were analyzed with a microplate reader. No statistically significant differences were observed when comparing the results of our automated ELISA on-chip against the ones obtained from a traditional ELISA on a microplate. Moreover, we demonstrated that it is possible to carry out the analysis of the colorimetric reaction by performing basic image analysis of photos taken with a smartphone, which constitutes a useful alternative when lacking specialized equipment or a laboratory setting. Our automated ELISA on-chip has the potential to be used in a clinical setting and mitigates some of the burden caused by testing deficiencies.