This paper presents an innovative approach to use real-time scheduling techniques for the automation of electric loads in Cyber-Physical Power Systems. The goal is to balance the electric power usage to achieve an optimized upper bound on the power peak load, while guaranteeing specific constraints on the physical process controlled by the electric loads. Timing parameters derived from the scheduling discipline of real-time computing systems are used to model electric devices. Real-time scheduling algorithms can be exploited to achieve the upper bound by predictably and timely switching on/off the devices composing the electrical system. The paper shows the relevance of electric load balancing in power systems to motivate the use of real-time techniques to achieve predictability of electric loads scheduling. Real-Time Physical Systems (RTPS) are introduced as a novel modeling methodology of a physical system based on real-time parameters. They enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. The model of the physical process considered in this work is characterized by uncertainties that are compensated by a suitable feedback control policy, based on the dynamic adaptation of real-time parameter values. A number of relevant relationships between real-time and physical parameters are derived.

In this study, an original strategy to control a mobile robot in a dynamic environment is presented. The strategy consists of two main elements. The first is the method for the online trajectory generation based on harmonic potential fields, capable of generating velocity and orientation references, which extends classical results on harmonic potential fields for the case of static environments to the case when the presence of a moving obstacle with unknown motion is considered. The second is the design of sliding-mode controllers capable of making the controlled variables of the robot track in a finite minimum time both the velocity and the orientation references.

The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and evaluate these problems, less attention has been dedicated to methods generating intentionally biased datasets, which could be used by data scientists to develop and validate unbiased and fair decision-making algorithms. To this end, this paper presents a novel method to generate a synthetic dataset, where bias can be modeled by using a probabilistic network exploiting structural equation modeling. The proposed methodology has been validated on a simple dataset to highlight the impact of tuning parameters on bias and fairness, as well as on a more realistic example based on a loan approval status dataset. In particular, this methodology requires a limited number of parameters compared to other techniques for generating datasets with a controlled amount of bias and fairness.