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The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive NO3− accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on 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. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a 28% to 53% increase in biomass growth under certain scenarios was attained.

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 modelled 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 customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on Dynamic Programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained Stochastic Dynamic Programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate Dynamic Programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.

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 natural language) into verifiable models, which can expose the design issues before the satellite manufacturing phase. This paper shows how the verification of complex system requirements can be performed via model checking. More specifically, a methodology is proposed which exploits the flexibility provided by the calculus of communicating systems to model complex system concurrent parts and their mutual interactions for verifying analytically their correctness, completeness, and consistency as prescribed by the system requirements. The proposed methodology is applied to the verification of a real satellite operational mode management specification. An abstraction reduction technique based on the selective mu-calculus logic is used to address the computational issues in model checking. It allows capturing and analyzing the parts of a satellite involved in the verification of a specific set of its system-level properties.

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 mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

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 addressed research efforts to their integration with the main grid by means of properly designedenergy storage systems(ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase theequipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles.

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 discretisation approach is applied to model resource allocation problems as a set of discrete-time BDPs, which are then integrated into one Markov decision process. The stochastic dynamics of the resulting system are also investigated. As a result, revenue management becomes a stochastic decision-making problem, where price managers can propose suitable prices to the allocation requests such that the maximum expected total revenue is obtained at the end of a predefined finite time horizon. Stochastic Dynamic Programming is employed to solve the related optimisation problem with the support of an ad-hoc Matlab-based application. Several simulations are performed to prove the effectiveness of the proposed model and the optimisation approach.

This work presents the current status of the design of Central Instrumentation and Control Systems for the IFMIF-DONES plant. IFMIF-DONES is the International Fusion Materials Irradiation Facility-DEMO Oriented NEutron Source, an accelerator-based neutron source producing a high-energy deuterons beam focused on a fast flowing liquid lithium jet to generate high-energy neutrons via stripping reactions. The intensity and irradiation volume is suitable to generate material irradiation data for design, licensing, construction and safe operation of the DEMO fusion reactor. After a general overview of the current status of the design of the overall architecture, a particular attention is given to the data management including the EPICS control framework, the general rules and criteria for the design and implementation of the HMI system, and the timing system solution composed of a three-layer distribution network to deliver time at each element of the plant that implements an optimal trade-off between timing performance and cost. Eventually, the next design steps and future work are outlined.

A framework for computing feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications for power line inspection tasks is proposed in this paper. The planner allows the formulation of complex missions that avoid obstacles and maintain a safe distance between drones while performing the planned mission. An optimization problem is set to generate optimal strategies that satisfy these specifications and also take vehicle constraints into account. Further, an event-triggered replanner is proposed to reply to unforeseen events and external disturbances. An energy minimization term is also considered to implicitly save quad-rotors battery life while carrying out the mission. Numerical simulations in MATLAB and experimental results show the validity and the effectiveness of the proposed approach, and demonstrate its applicability in real-world scenarios., Comment: 8 pages, 12 figures, journal paper

Designing artificial cyber-agents able to interact with human safely, smartly and in a natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of co-operatively interacting with people in order to fulfil common joint tasks in a multitude of different applications. This is particularly relevant in the context of healthcare applications. Indeed, the use has been proposed of artificial agents interacting and coordinating their movements with those of a patient suffering from social or motor disorders. Specifically, it has been shown that an artificial agent exhibiting certain kinematic properties could provide innovative and efficient rehabilitation strategies for these patients. Moreover, it has also been shown that the level of motor coordination is enhanced if these kinematic properties are similar to those of the individual it is interacting with. In this paper we discuss, first, a new method based on Markov Chains to confer "human motor characteristics" on a virtual agent, so as that it can coordinate its motion with that of a target individual while exhibiting specific kinematic properties. Then, we embed such synthetic model in a control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behaviour of a specific human performing a joint motor task with one or more individuals., Comment: 16 pages, 13 figures

In this letter, we study the problem of controlling via pinning the motion of nonlinear network systems of any order whose dynamics are in controllable canonical form. Different from existing works that either focus on spontaneous synchronization, assume linear dynamics or rely on dynamics cancellation, here we provide a constructive method to prove pinning controllability towards the desired trajectory selected by the pinner. We introduce an algorithmic procedure that associates to any connected topology a suitable Lyapunov function for the network system. The approach is demonstrated on an illustrative example.

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 operation, to smooth the wind power output, and to track a load demand subject to a variety of constraints on the system model. Based on a modeling adopting mixed-integer constraints and dynamics, the problem of output power fluctuations and smooth tracking of load demand is solved through the implementation of an optimal controller which follows a sequential optimization technique. We illustrate the effectiveness of the proposed controller with a simulation example, employing real equipment data and wind profiles.