Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust is required under heavy loads, such as wheelchairs, medical beds, and lifting tables. Due to the remarkable ability to exert forces and good precision, they are used classic control systems and controls of high-order. Still, they present difficulties in changing their dynamics and are designed for a range of disturbances. Therefore, in this paper, we present the study of an electric linear actuator. We analyze the positioning in real-time and attack the sudden changes of loads and limitation range by the control. It uses a general-purpose control with self-tuning gains, which can deal with the essential uncertainties of the actuator and suppress disturbances, as they can change their weights to interact with changing systems. The neural network combined with PID control compensates the simplicity of this type of control with artificial intelligence, making it robust to drastic changes in its parameters. Unlike other similar works, this research proposes an online training network with an advantage over typical neural self-adjustment systems. All of this can also be dispensed with the engine model for its operation. The results obtained show a decrease of $42\%$ in the root mean square error (RMSE) during trajectory tracking and saving in energy consumption by $25\%$. The results were obtained both in simulation and in real tests.

This work presents a background subtraction method based on Independent Components Analysis (ICA) implemented on a Field-Programmable Gate Array (FPGA) System-on-a-Chip (SoC) with an embedded processor. A previous work showed background subtraction utilizing ICA achieves better results than Mixture Of Gaussians (MOG) when the background is dynamic. However, that approach was developed for a computer and its proposed mean of using ICA is too complex, requiring a high-end computer for implementation. With the purpose of extending this approach to current embedded vision systems, a method is developed for an FPGA-SoC with embedded processor. This recent technology complements the parallelism of FPGAs with the general purpose computing of processors, maintaining a small footprint and a low power consumption. In this work, background is continuously updated by estimating the mean with Expectation-Maximization (EM), foreground is extracted via FastICA, and movement is determined by a threshold based on standard deviation. The herein presented approach to these algorithms allows exploiting the architecture of FPGA-SoCs, however, there are alternatives which could replace these algorithms. The developed method was tested on two image sequences and the accuracy of movement detection was measured by the precision and the recall.