The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks.

Target tracking is a key component of control systems with applications in various domains. Several examples of commercial applications may be given, which benefit from this technology: the development of car collision avoidance systems which must detect and track potential obstacles and hazards, or the development of UAVs that can track and record the evolution of athletes. For the purpose of our research, this technology was developed and tested as part of a less complex application. The current work describes a simple yet practical implementation of vision-based control for a mobile robot system. In the experiment, we used a mobile robot as target and a similar robot as follower. To achieve the tracking task, the used strategy involves the detection of a specific visual object mounted on the target, by extracting its features which are then used in issuing control commands within a remotely-closed loop. A Deep Learning approach is used for object detection, incorporating a detector model into the strategy while preserving an explicit controller in the overall scheme. The carried experiment has proven that this new approach provides the expected results, which make it a suitable tool for development of larger scale applications of control systems.

Implementation of control systems for metal machining process is leading to better quality of products, increase of productivity and decrease of environmental impact. These control systems are analyzing various types of data, acquired by sensors and IoT devices, to predict, to make classifications and to generate decisions. These imply all machining process phases. By analyzing the surface roughness, the machining process can be controlled, the decision if the resulting product can be accepted or should be rejected can be taken, and decisions regarding maintenance tasks can be generated, tasks that may imply the replacement of cutting tools. This way, the control system integrates predictive maintenance features which in-crease its complexity and value. In our research we propose a new classification algorithm to determine the surface roughness, classification that is later used as input to predictive and decision algorithms of the control system of the machining process. Traditional methods used to determine the surface roughness require highly skilled specialists and in-vestigations with the help of high-quality measuring equipment, both of which are not in the grasp of every company. The method we propose is intended to become an affordable and reliable tool for everybody. Thus, we decided to use a low-cost microscope to acquire the images that will be analyzed to determine the surface roughness. For classification we used Feed-Forward and Autoencoder Artificial Neural Networks on samples, splitting material roughness into three categories. Using this approach we achieved over 88% recognition of surface roughness categories. Our best run gave us an average error of 0.79%. These results make this method a viable tool for control systems implementations for metal machining.