Luigi Glielmo

• PhD in Automatic Control
• Professor (Full) at University of Sannio

This article proposes a data-driven decentralized control scheme for a battery energy storage system, “shared” among residential PV households characterized by their respective uncontrollable demand and PV generation. The households are connected to the grid via the point of common coupling and are accordingly billed by the utility company. We firs...

This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To addres...

Energy storage systems are key to take advantage of the potential of renewable energy. Renewable energy communities endowed with a Shared Battery Energy Storage System (SBESS) have been proposed as a key factor to efficiently exploit distributed renewable generation. However, ensuring renewable energy communities are not only environmentally friend...

In this letter, we study the "decentralized" scheduling of an energy storage system "shared" among residential households. In particular, we consider the households as learning agents and model their interaction as a Markov Game. To address the challenges associated with the non-stationary nature of the multi-agent learning, we propose a consensus-...

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final...

Hydrogen is a promising energy vector for achieving renewable integration into the grid, thus fostering the decarbonization of the energy sector. This paper presents the control platform architecture of a real hydrogen-based energy production, storage, and re-electrification system (HESS) paired to a wind farm located in north Norway and connected...

Hydrogen is a promising energy vector for achieving renewable integration into the grid, thus fostering the decarbonization of the energy sector. This paper presents the control platform architecture of a real hydrogen-based energy production, storage, and re-electrification system (HESS) paired to a wind farm located in north Norway and connected...

Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization in order to preserve safety and performance. In indoor scenarios, technologies such as Visual Simult...

We present a novel approach for solving path-planning problems using a state-of-the-art data-driven deep learning technique. Although machine learning has been previously utilized for path planning, it has proven to be challenging due to the discrete nature of search algorithms. In this study, we propose a deep learning-based algorithm for path pla...

We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a giv...

A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle's actuation li...

A novel prize-winner algorithm designed for a path following problem within the Unmanned Aerial Vehicle (UAV) field is presented in this paper. The proposed approach exploits the advantages offered by the pure pursuing algorithm to set up an intuitive and simple control framework. A path fora quad-rotor UAV is obtained by using downward facing came...

This paper proposes a fixed-time distributed robust optimization approach for solving economic dispatch problems. Based on an integral sliding mode control scheme, the proposed multi-agent system converges to an optimal solution to an economic dispatch problem before a fixed time. In addition, the proposed multi-agent system can suppress the distur...

We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network or Random Forest which, for a...

Distributed control architectures are paving the way for the next generation of energy system infrastructures, primarily due to the combination of smart grid technologies and energy market deregulation. The new framework offers increased privacy and decision autonomy for the end-users, but it poses challenges due to the lack of direct control of th...

Mini-drones can be used for a variety of tasks, such as weather monitoring, package delivery, search and rescue, and recreation. Their uses are mostly restricted to outside locations with access to the Global Positioning System (GPS) and/or similar systems since their usefulness, safety, and performance substantially rely on ubiquitously accurate p...

A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle's actuation li...

This article aims to develop an optimal decision support system that indicates when to initiate fertilizing based on the nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. Numerous studies with...

Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault models are not usually available for complex productions plants and production data are usually unla...

Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promot...

This study proposes a multi-level model predictive control (MPC) for a grid-connected wind farm paired to a hydrogen-based storage system (HESS) to produce hydrogen as a fuel for commercial road vehicles while meeting electric and contractual loads at the same time. In particular, the integrated system (wind farm + HESS) should comply with the “fue...

This study proposes a multi-level model predictive control (MPC) for a grid-connected wind farm paired to a hydrogen-based storage system (HESS) to produce hydrogen as a fuel for commercial road vehicles while meeting electric and contractual loads at the same time. In particular, the integrated system (wind farm + HESS) should comply with the "fue...

It has become imperative for the power and energy engineers to look out for the renewable energy sources such as sun, wind, geothermal, ocean and biomass as sustainable, cost-effective and environment friendly alternatives for conventional sources. However, since they are inherently unreliable, during the last decades the scientific community addre...

In this work, strategies to devise an optimal feedback control of probabilistic Boolean control networks (PBCNs) are discussed. Reinforcement learning (RL) based control is explored in order to minimize model design efforts and regulate PBCNs with high complexities. A Q-learning random forest (QLRF) algorithm is proposed; by making use of the algor...

This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus da...

Development of an MPC strategy for regenerative braking in (TTR) hybridized electric vehicles

Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landi...

In this paper, a model-free co-design scheme of triggering-driven controller is proposed for probabilistic Boolean control networks (PBCNs) in order to achieve feedback stabilization with minimum controller efforts. Specifically, Q-learning (QL) algorithm is exploited to devise a self-triggered strategy wherein the controller update time is compute...

Backward reachability (also termed controllability) has been extensively studied in control theory, and tools for a wide class of systems have been developed. Nevertheless, assessing a backward reachability analysis or synthesis remains challenging as the system dimension grows. In this paper we study the backward reachability problem for large sca...

An important module in driver assistance systems is the lane departure warning which warns the driver when the vehicle begins to cross out of its lane inadvertently. A novel lane detection algorithm designed for lane departure warning system is presented in this paper. The proposed approach exploits the advantages offered by tree search to set up a...

Nowadays, many vehicles are connected to the Internet for providing enhanced or additional functionalities to drivers. However, the existing vehicle communication protocols are not designed for cybersecurity. As a result, Autonomous Vehicles (AVs) are exposed to hacker attacks which could jeopardize the safety of passengers. In this paper, a novel...

Pedestrian collision avoidance is a relevant safety aspect for autonomous driving systems operating in urban scenarios. This paper presents a Reinforcement Learning approach to endow the resulting agent with the following two competing capabilities: managing unexpected pedestrian crossings and tracking a specific trajectory. In particular, we use t...

Due to the increasingly dominant climate change, "green" energy sources that do not contribute to further damage of the environment play an emerging and relevant role. A big advantage is that this kind of energy can be generated not only on a large scale, such as by wind turbines or hydroelectric power plants, but also by individual households thro...

The rationale of shifting towards green energy, along with the cost reduction and the increasing capacity of lithium-ion batteries, has motivated the end-users to go for energy storage systems integrated with solar technology solutions. Such systems provide the end-users with greater flexibility, thereby enhancing their role as prosumers in a range...

A novel prizewinner algorithm designed for a path following problem within the Unmanned Aerial Vehicle (UAV) field is presented in this paper. The proposed approach exploits the advantages offered by the pure pursuing algorithm to set up an intuitive and simple control framework. A path for a quad-rotor UAV is obtained by using downward facing came...

In recent years, one of the highest challenges in the field of artificial intelligence has been the creation of systems capable of learning how to play classic games. This paper presents a Deep Q-Learning based approach for playing the Snake game. All the elements of the related Reinforcement Learning framework are defined. Numerical simulations fo...

Demand side management strategies are used in many application scenarios in order to mitigate high peak demands and reduce them to comply with the availability of electricity, which has many implications such as, e.g., costs minimization and efficiency increase, among the others. In this paper, we present a game-theoretic approach for the demand si...

Deregulation of the power network, along with integration of renewable energy resources and energy storage systems, anticipates an increased decision making autonomy to the end-users. Curtailing the peak, also known as peak shaving, is one such aspect where the end-users could play a significant role in making the grid more resilient and robust. In...

In this letter, we model the day-ahead price-based demand response of a residential household with battery energy storage and other controllable loads, as a convex optimization problem. Further using duality theory and Karush-Kuhn-Tucker optimality conditions, we derive a sufficient criterion which guarantees non-simultaneous charging and dischargi...

Steel making industries exhibit extreme working conditions characterized by high temperature, pressure, and production speed as well as intense throughput. Due to high economic and energy investments of the overall production process, an intense and expensive preventive maintenance program is adopted to avoid breakdowns. Steel making process would...

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a...

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addre...

The current deliverable explains the formulation of a Model Predictive Control (MPC) policy for a wind-hydrogen plant in fuel-production use cases within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS. In the fuel production control algorithm, hydrogen production objectives are two-fold: to deliv...

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addre...

In this paper, we study the control of probabilistic Boolean control networks (PBCNs) by leveraging a model-free reinforcement learning (RL) technique. In particular, we propose a Q-learning (QL) based approach to address the feedback stabilization problem of PBCNs, and we design optimal state feedback controllers such that the PBCN is stabilized a...

This paper proposes an output power smoothing strategy for a grid-connected wind-hydrogen plant. An Energy Storage System (ESS) composed of an electrolyzer and a fuel cell is used to smooth the fluctuating output power of the wind plant. The aim of this study is to propose a multi-objective optimization model for joint wind farm and energy storage...

In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean control networks (PBCNs). In particular, a double deep- $Q$ network (DD $Q\text{N}$ ) approach is firstly proposed to address the output tracking problem of PBCNs, and optimal state feedback controllers are obtained such that...

Purpose: The purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine performances. Design/methodology/approach: In developing a complete CBM strategy, two main diffic...

Value function approximation has a central role in Approximate Dynamic Programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the “Multi-trajectory Greedy LSTD” (MG-LSTD). It is an exploration-enhanced recu...

In this paper, the set stabilization of switched Boolean control networks (SBCNs) under sampled-data feedback control is addressed. Here, the control input is switching signal-dependent, and SBCNs can switch only at the sampling instants. First, the sampled point control invariant subset (SPCIS) of SBCNs is defined, and an algorithm is provided to...

In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader's objective function, while controlling the reaction function o...

In this paper, the design of all possible switching signal-dependent state feedback and output feedback stabilizers for switched Boolean control networks (SBCNs) under arbitrary switching signal is investigated. By making use of the algebraic state-space approach to SBCNs, necessary and sufficient conditions for the stabilization at a given equilib...

This paper presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a non-convex NPhard problem with uncertainties due to renewable generation and d...

The current deliverable explains the formulation of a multi-level Model Predictive Control (MPC) policy for a wind-hydrogen plant in mini-grid use cases within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS. In the mini-grid use case, hydrogen production is required in order to store temporary su...

he environmental impact of transportation systems is considered a crucial point when new solutions must be adopted to enhance life quality in urban areas. All countries are seriously supporting and adopting green transportation systems according to increasingly stringent environmental quality targets. In this context, this paper introduces an innov...

The current deliverable regards the formulation of a Model Predictive Control (MPC) policy for hydrogen-based storage plants in the energy storage use case to be implemented within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS. The proposed policy relies in part upon the analysis and the modelli...

This paper reports the outcome of an industrial research on data-driven Condition Based Maintenance (CBM) for the film cutting group of labeling production lines. Objective of the study has been the prediction of erroneous labels cut. The large number of variables involved in thin labels cut (thickness comprised within 30 μm and 38 μm) and the high...

In this paper, a novel model for price management systems in resource allocation problems is proposed. Stochastic customer requests for resource allocations and releases are modeled as constrained parallel Birth-Death Processes (BDP). We address both instant (i.e., the customer requires a resource to be allocated immediately) and advance (i.e., the...

Probabilistic Boolean control network (PBCN) is a discrete-time dynamical system comprised of a collection of Boolean control networks (BCNs) and switching among them in a stochastic manner. In this paper, the output tracking control of PBCNs is investigated via state feedback and output feedback control. By resorting to the algebraic state-space r...

The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction,...

In this paper, resource allocation problems are formulated via a set of parallel Birth-Death processes (BDP). This way, we can model the fact that resources can be allocated to customers at different prices, and that customers can hold them as long as they like. More specifically, a discretization approach is applied to model resource allocation pr...

Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for formulating, modeling, and solving decision making problems under uncertainty. In real-world applications, the applicability of DP is limited by severe scalability issues. These issues can be addressed by Approximate Dynamic Programming (ADP) techniques. ADP methods...

In this paper, the output tracking control design of switched Boolean control networks (SBCNs) is investigated via state feedback and output feedback control. The algebraic state-space representation method which resorts to the semi-tensor product (STP) of matrices is utilized; necessary and sufficient conditions for the solvability of the output t...

Utility companies are an integral part of smart grid, providing the consumers with a broad range of energy management programs. The quality of service is based on the measurements obtained from smart metering infrastructures, which can further be improved by sensing at finer resolutions. However, sensing at higher resolutions poses serious challeng...

Based on binary logic, this study presents a new framework to analyze the dynamics of Boolean control networks (BCNs) and investigates the basic problem of state-space approach without using the semi-tensor product (STP). The logical form of BCNs is transformed into a discrete-time bilinear system by resorting to Khatri-Rao product and Boolean mint...

Control of infectious diseases using bacteriophage therapy is regaining interests in modern medicine and systems biology as an alternative treatment for antibiotic-resistant bacteria. A key issue is to control the phage's replication process: indeed, phage may switch either towards lysis state or lysogeny state during its reproduction due to some c...

The intermittent nature of wind energy combined with the penalty deviations adopted in several electricity regulation markets explains the difficulty of this clean energy in playing a major role in the energy system. Coupling the wind farm with advanced energy storage systems represents, in principle, a good solution for these problems. To date, se...

Maintaining the optimal performance of cell processes and organelles is the task of auto-regulatory systems. Here we describe an auto-regulatory device that helps to maintain homeostasis of the endoplasmic reticulum (ER) by adjusting the secretory flux to the cargo load. The cargo-recruiting subunit of the coatomer protein II (COPII) coat, Sec24, d...

Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in the investment casting manufacturing industry, where huge variety of heterogeneous data, related to different producti...

The Haeolus project publishes today one of it first major public deliverables, a report on Dynamic Models for Hydrogen Production and Storage Plants. The report has been authored by the Haeolus team at the University of Sannio, Italy, and describes how to model a hydrogen plant in connection with a wind power plant. The wind is an intermittent, dif...

In recent years, there has been a strong activity in the so-called precision agriculture, particularly the monitoring aspect, not only to improve productivity, but also to meet demand of a growing population. At a large scale, precise monitoring of cultivated fields is a quite challenging task. Therefore, this paper aims to propose a survey on tech...

This investigation proposes a CPES architecture and model for engineering energy management application for smart grids. In particular, the investigation considers the implementation of the power systems state estimator (PSSE). The CPES architecture has three layers: physical, monitoring and applications. The physical layer consists of the grid and...

The problem of managing the price for resource allocation arises in several applications, such as purchasing plane tickets, reserving a parking slot, booking a hotel room or renting SW/HW resources on a cloud. In this paper, we model a price management resource allocation problem with parallel Birth-Death stochastic Processes (BDPs) to account for...

In this paper we investigate the reachability and controllability of delayed switched Boolean control networks (DSBCNs). By resorting to the algebraic state space representation method built using semi-tensor product (STP) of matrices, we provide several necessary and sufficient conditions for these properties to hold which are based on input-state...

X-linked recessive diseases are genetic disorders caused by gene's abnormalities placed on the X chromosome. Due to differences between males and females in sex chromosomes, the transmission mechanisms of these diseases vary in the two sexes. Other than the results of the well-known patterns of inheritance, the current spread of genetic disorders i...

Satellites are nowadays complex systems and can be considered as components of larger mission-level systems of systems. The increasing complexity of space mission objectives is actually complicating the requirement engineering process. It is generally understood that space system engineers should translate system-level requirements (elaborated in n...

Pressure-reducing valves (PRVs) are often used in water distribution networks (WDNs) to regulate pressure for leakage reduction. Optimal management would require the pressure to be constant and as low as possible at the WDN critical node. Such operating conditions can be achieved by means of real-time control (RTC) of the PRVs. Because the pressure...

Population genetics is a scientific discipline that has extensively benefitted from mathematical modelling; since the Hardy-Weinberg law (1908) to date, many mathematical models have been designed to describe the genotype frequencies evolution in a population. Existing models differ in adopted hypothesis on evolutionary forces (such as, for example...

Two Major challenges in securing reliable Optimal Power Flow (OPF) operations are: (i) fluctuations induced due to renewable generators and energy demand, and (ii) interaction and interoperability among the different entities. Addressing these issues requires handling both physical (e.g., power flows) and cyber aspects (computing and communication)...

This study addresses a robust counterpart of the deterministic mean field control in a multi-agent system. A decentralised mean field algorithm is proposed to solve a min-max control problem for a large population of heterogeneous agents. In the proposed leader following scheme, the leader tracks a reference signal which is unknown to the followers...

Different drivers are nowadays leading spacecraft toward an increased level of on-board autonomy. In this paper, we survey model-based techniques as a vehicle to implement highly autonomous on-board capabilities for spacecraft mission planning and execution. In this respect, spacecraft reconfiguration approaches based on Markovian Decision Process...

This paper proposes a stochastic optimal controller for networked control systems (NCS) with unknown dynamics and medium access constraints. The medium access constraint of NCS is modelled as a Markov Decision Process (MDP) that switches modes depending the channel access to the actuators. We then show that using the MDP assumption, the NCS with me...

In each mission phase, a satellite is characterized by a well-defined set of modes. They define a clear configuration of the spacecraft subsystems and have specific operational implications. In this paper, we present an approach for geostationary earth observation satellite mode management during both the Launch and Early Orbit phase and the full o...

A common strategy for leakage reduction in Water Distribution Networks (WDNs) is the use of Pressure Reducing Valves (PRVs). As well known, a relationship between pressure and water losses can be established, according to which reducing pressure results in reduced losses. In many cases pressure is greater than the minimum required for adequate serv...

Spacecraft on-board autonomy is an important topic in currently developed and future space missions. In this study, we present a robust approach to the optimal policy of autonomous space systems modeled via Markov Decision Process (MDP) from the values assigned to its transition probability matrix. After addressing the curse of dimensionality in so...

In this chapter we study the access time on random walks, i.e., the expected time for a random walk starting at a node \(v_i\) to reach a node \(v_j\), an index that can be easily calculated resorting to the powerful tools of positive systems. In particular, we argue that such an index can be the base for developing novel topological descriptors, n...

Operating heating power plant (DHPP) with fluctuating load is a complex problem. Thermal energy storage (TES), flexible loads, and operating constraints compound this complexity further. This investigation focuses on the design of a model predictive controller (MPC) that reduces the operating and maintenance cost in a DHPP, considering TES and flex...

This paper proposes a stochastic optimal controller for networked control systems (NCS) with unknown dynamics and medium access constraints. The medium access constraint of NCS is modelled as a Markov Decision Process (MDP) that switches modes depending the channel access to the actuators. We then show that using the MDP assumption, the NCS with me...