In this paper, we investigate the use of Physics-Informed Neural Networks (PINNs) for the solution of an optimal control problem related with the optimal control of a stationary electric storage system (ESS) installed in a service station for plug-in electric vehicles (PEVs). The ESS is used to balance the PEVs charging power, in order to mitigate the impact on the grid, and keep low the power flow at the point of connection with the grid. The proposed PINN is trained in order to learn the optimality conditions of the optimal control problem so that, after training, it can provide the solution with no significant computation effort. This one represents a promising alternative to the analytical computation of the optimal control (which is possible only in very simple settings), and to the solution of the optimal control problem with numerical methods, which requires significant time in the more complex and realistic settings. Numerical simulations are presented to evaluate the effectiveness of the trained PINN in solving the optimal control problem.

In digital transformation, Industrial Data Space (IDS) is a key enabler for industry 4.0 to improve the industrial process, efficiency, and energy consumption by exploiting learning-based techniques. The present paper focuses on improving the decision-making process in complex industrial environments by developing a Deep Reinforcement Learning (DRL) based real-time assistant. Mainly, we address a use case from the space industry to improve the launcher throughput and efficiency and reduce cost by optimally managing the industrial resources. A mathematical formulation of the Industrial Production System (IPS) and a simulated environment are developed to train the DRL-based Proximal Policy Optimization (PPO) agent. The proposed method is scalable and in line with the dynamic nature of the industrial production systems to overcome the domain-dependent heuristics extensively used in the manufacturing industry. Furthermore, simulation results show that the proposed method can provide industrial operators and managers with a real-time decision support system to increase the Return on Assets.

Traffic congestion is among the worst causes of pollution, and the time spent in traffic can cost the world tens of billions of dollars every year. Solutions to mitigate this problem are at hand thanks to the advent of advanced control techniques and artificial intelligence (AI). Traditional traffic light control strategies based on fixed timing of the green, yellow and red phases are simple to implement, but at the same time very inefficient, in particular for busy intersections. This paper discusses both a model predictive control (MPC) approach and a model-free deep reinforcement learning (DRL) algorithm for controlling the traffic lights at a single intersection, with the aim of improving the traffic flow. Firstly, a detailed linear mathematical model of an intersection is formulated and successively tested in a MPC framework; secondly, a DRL algorithm is proposed and verified by comparing it with the currently implemented baseline controller. Finally, the results for the three approaches, MPC, DRL and the baseline controller, are validated through the SUMO (Simulation of Urban Mobility) microscopic traffic simulator.

This paper discusses the problem of assembly line control and introduces an optimal control formulation that can be used to improve the performance of the assembly line, in terms of cycle time minimization, resources' utilization, etc. A deterministic formulation of the problem is introduced, based on mixed-integer linear programming. A simple numerical simulation provides a first proof of the proposed concept.

This paper presents a decentralised control strategy for the management of simultaneous charging sessions of electric vehicles. The proposed approach is based on the model predictive control methodology and the Lagrangian decomposition of the constrained optimization problem which is solved at each sampling time. This strategy allows the computation of the charging profiles in a decentralised way, with limited information exchange between the electric vehicles. The simulation results show the potential of the proposed approach in relation to the problem of shaving the aggregated power withdrawal from the electricity distribution grid, while still satisfying drivers’ preferences for charging.

One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.