This article explores the management of automated guided vehicles within smart manufacturing systems, presenting an architecture designed to handle various stages in a fully automated production system. Trajectory planning is managed using a potential field controller, coupled with a time-of-arrival optimization for vehicles heading to shared resources, exploiting the concepts of virtual platoons. This approach optimizes arrival times at resources and minimizes energy consumption, prioritizing other elements directed toward currently available resources within the system. In addition, a decision-making algorithm schedules vehicles needing recharging due to low battery levels, ensuring uninterrupted production without overloading recharging stations and guaranteeing system efficiency. The proposed architecture is tested on three production orders of increasing complexity. Results demonstrate that the use of virtual platooning simplifies the dynamic trajectory generation of the potential field controller and, consequently, significantly reduces energy consumption and waiting times at shared resources, while reducing the variability on the production chain's makespan.

Power demand has increased in recent years, posing a significant challenge to power systems. The use of clean energy generated by wind farms (WF) for the operation of pumped hydroelectric energy storage (PHES) is often advocated as a hybrid renewable energy system for peak load reduction, defined as WF-PHES. The objective of this paper is to present a comprehensive optimal operational scheduling strategy-based algorithm for dynamically shaving or reducing peak power loads. For this purpose, a finite horizon scheduling optimization problem has been formulated to optimally control the real-time operation of the WF-PHES that incorporates both predictions of the power load and winds. The main aim of the proposed framework is the scheduling of the whole system operation based on predicted wind speeds and power load profile, while respecting the operational constraints. A three-layered ANN model has been used to forecast the wind speeds and the power load based on historical data. A mathematical decision model considering the power load-shaving and reduction needs of the network for the summer and winter seasons has been developed. The proposed model has been implemented and applied to a case study. The proposed framework has been compared to two distinct non-linear optimization methods: Pyomo with IPOPT solver and Genetic Algorithm (GA) to prove its performance and effectiveness over extensive numerical simulationsNote to Practitioners—This paper is motivated by the problem of reducing the long-term peak load on the electricity grid. A hybrid efficiency system was developed by combining a renewable energy source (wind) with a large-scale storage system. The optimal system operation strategy given by the optimization model shows important advantages in providing a clear view for those making decisions regarding this type of energy system. The developed decision algorithm may be taken as a practical solution to address the development challenges of wind energy and support utilities and power grid managers to increase the penetration of renewable generators as well as reduce peak power loads.

Shortly, power distribution grids will incorporate large amounts of distributed energy resources and flexible loads, allowing the operation of a portion of the network in islanded mode to increase the reliability and resilience of the whole power system. A fully distributed robust model predictive control (MPC) strategy for voltage and frequency regulation in interconnected distribution grids is stated. Each grid node represents a collection of prosumers with a large active and reactive power regulation capacity. The advantages of this approach rely on the capability to afford any type of uncertainties, without making any assumption on the probability density function, on distributed generation and load nowcasting. We propose a two-stage architecture: at the first stage, an MPC approach, based on the distributed alternating direction method of multipliers (dADMM), is performed, considering the data nowcasting; instead, the second stage (based on robust distributed team decision theory) takes as input the trajectory of the first stage to compensate the noise that affects the system. The developed architecture has been tested on a modified IEEE5 bus system, considering multiple loads and renewable generation.

Wheeled Mobile Robots (WMR) with the assistance of Information Communication Technology (ICT) can navigate and perform some tasks in an uncontrolled environment. They can experience some problems in task management if they can not localize themself in indoor/outdoor environments. Different algorithms can be exploited to evaluate the proper control law to track a default path. In this paper, a path-tracking algorithm for a WMR has been tested for an indoor environment, with a delimited area spanned by localization tags. The WMR must check some waypoints during its path until the last one, where the WMR re-start the simulation. In this work, the control law applied for the experiments is the Non-Linear Model Predictive Controller (NMPC). The control algorithm is tested using two experiments, the first one is based on the robot movements in a simulation environment using the Gazebo tools, and the second one is related to a real context where the mobile robot moves in an indoor environment.

Roundabouts pose complex challenges for autonomous vehicles. Approaching and crossing them safely requires a significant amount of information, much of which is typically unavailable. With autonomous vehicles becoming increasingly prevalent on the roads, new approaches are necessary to address these upcoming issues. While platoons and distributed control have been extensively studied in the past decade, roundabouts have received less attention. This paper presents a distributed Nonlinear Model Predictive Control (NMPC) approach using the Alternating Direction Method of Multipliers (ADMM) to utilize virtual platooning and enhance the throughput of a roundabout without requiring approaching vehicles to come to a stop. Instead, it manages the velocity of each vehicle while maintaining a safe distance. The proposed approach is validated through two case studies.

In the European Rail Traffic Management System (ERTMS), the Route Control Centre System (RCCS) supervises the distance between consecutive trains and generates movement authorities, i.e. the permission for a train to move to a specific location within the constraints of the infrastructure and with supervision of speed. In this work, a control model aimed at determining the speed and position of a train platoon within a sector of the rail network is presented. The central controller, i.e. the RCCS, receives information about the current position and speed of trains and it sends them decisions about optimal corrective actions for each train. Priorities of trains are handled to respect the planned timetable, taking into account the train dynamics, limitations in divergences of positions, speeds, and tractive effort, as well as minimum distances between consecutive trains. The control approach is based on a quite innovative linear quadratic regulator allowing the definition of stochastic constraints. The validation of the model is based on data collected from a RCCS for a Section of the high-speed Paris-London line.

This paper introduces a comparison between a decentralized Proportional Integral Derivative (PID) controller and a centralized Linear Quadratic Tracking (LQT) controller to automatise the exchange of two inner vehicles inside a platoon moving on a straight path. Lomonossoff’s model is used to represent vehicle’s longitudinal dynamics. A case study is presented to demonstrate the effectiveness of both controllers respectively on nonlinear and linearized model.

This paper proposes a solution to dynamically adjust vehicle platoon trajectories. The goal of the control algorithm is to keep the optimal interdistance between adjacent vehicles proceeding at cruising speed on a straight road. After a proposal of the interdistance required between neighboring vehicles, a robust decentralized controller based on a linear control law provides the speed profile for each component of the platoon. Its objective is to minimize the divergence in space in respect to the planned trajectories while assuring a safe span between adjacent members of the platoon. The results on a limited instance demonstrate the effectiveness of the proposed approach.

In the last years, the manufactured vehicles were designed to focus on prevention of some risky situations caused by a human driver. The aim of this paper is to illustrate the design and implementation of a BCI system which can detect the arm movements by the EEG signal during a simulated driving session. The proposed approach to realize a classifier able to recognize the arm movement by EEG feature analysis is based on the consecutive application of a Time Delay Neural Network (TDNN) and a Pattern Recognition Neural Network (PRNN). Preliminary tests are shown on three different participants between 24 and 45 years old.

Electricity represents the main promising option to decarbonize the transport sector, above all, for the private car use. Therefore, the diffusion of electric vehicles (EVs) in the automotive market is constrained to an adequate deployment of the energy distribution infrastructures. The paper proposes a methodology to identify the optimal locations of Electric Charging Points (ECPs) for EVs, considering both location and size. An original version of the facility location problem has been developed by a mixed-integer mathematical model. The customer's choices, in a competitive environment, have been modelled by the logit and the gravity models. A realistic application of the proposed model is explored and discussed to a case study located in Savona (Liguria Region, Italy).