Distributed Model Predictive Control for Housing with Hourly Auction of Available Energy

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. According to the hourly power demand profile, each house assigns a priority level that indicates how much is willing to bid in auction for consume the limited clean resource. This procedure allows the bidding value vary hourly and consequently, the agents order to access to the clean energy also varies. Despite of power constraints, all houses have also thermal comfort constraints that must be fulfilled. The system is simulated with several houses in a distributed environment

LSTM neural networks: Input to state stability and probabilistic safety verification

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution of LSTM can be recast in state space form, resulting in a time-invariant nonlinear dynamical system. A sufficient condition guaranteeing the Input-to-State (ISS) stability property of this class of systems is provided. The ISS property entails the boundedness of the output reachable set of the LSTM. In light of this result, a novel approach for the safety verification of the network, based on the Scenario Approach, is devised. The proposed method is eventually tested on a pH neutralization process

Plug-and-play distributed state estimation for linear systems

This paper proposes a state estimator for large-scale linear systems described by the interaction of state-coupled subsystems affected by bounded disturbances. We equip each subsystem with a Local State Estimator (LSE) for the reconstruction of the subsystem states using pieces of information from parent subsystems only. Moreover we provide conditions guaranteeing that the estimation errors are confined into prescribed polyhedral sets and converge to zero in absence of disturbances. Quite remarkably, the design of an LSE is recast into an optimization problem that requires data from the corresponding subsystem and its parents only. This allows one to synthesize LSEs in a Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of the whole estimator requires at most the design of an LSE for the subsystem and its parents. Theoretical results are backed up by numerical experiments on a mechanical system

Distributed non-cooperative MPC with neighbor-to-neighbor communication ?

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Clinical, pathological and microbiological profiles of spontaneous enteropathies in growing rabbits

[EN] In a rabbit production facility, health monitoring for enteropathies was performed in 15 production cycles for 20 mo. For each cycle, up to a hundred 35 d old rabbits weaned the same day were randomly selected, reared in the same fattening unit, but separately from the source batch and fed with the same feed except for antimicrobial supplementation. Clinical symptoms and enteric lesions of the selected group were recorded, using two checklists with binomial response (yes/no answer to a list of 54 clinical and enteric variables). The day after weaning, one week later, at the beginning of the enteric symptoms and 4-5 d after the start of the symptoms, inocula from the small intestine and caecum of selected animals were subjected to microbiological, C. spiroforme, Eimeria oocyst and rotavirus antigen detection tests. Representative samples of E. coli and C. perfringens isolates were tested, respectively, for serotype, biotype, eae, afr/2 genes and for a, b1, b2, e, i and enterotoxin toxin genes. The answers to the clinical-pathological variables were subjected to statistical analysis with a cluster analysis programme in order to obtain homogeneous, statistically significant groups of diseased animals (clusters). Then, the clusters were statistically associated with the laboratory outcomes. The cluster to which the enterotyphlitis lesions significantly contributed was associated with E. coli detection, E. coli O103 serotype detection and C. spiroforme ("several elements" variable). C. spiroforme ("rare elements" variable) was significantly associated with a cluster, characterised by a pathological profile consisting of bloating/rumbling noise and liquid content in stomach and caecum, without enteric inflammation. C. perfringens was significantly associated with a cluster, characterised by a pathological profile consisting of dilation/liquid content of small intestine, caecal impaction and mucoid content in the colon. Eighteen out of twenty-fi ve C. perfringens strains, examined for their toxin genotypes, proved to be toxin type A, while 7 out of 25 strains showed the a and b2 toxin genes in combination. The rotavirus antigen and Eimeria oocysts were detected from healthy rabbits (specimens of the day after weaning and one week later) in about 15% of specimens examined, but their presence in the sick animals was not significantly associated with any cluster.This study was supported by a financial contribution from Avitalia, Unione Nazionale Associazioni di Produttori Avicunicoli, Forlì, Italy, as part of the programme entitled “Miglioramento della qualità, della gestione dell’offerta delle produzioni cunicole e di rafforzamento dei rapporti di filiera. Azione 4.3”. Our thanks go to breeder Leta Covelli and Dasco srl for supplying the rabbits, to our colleague Romolo Salini and to Fabrizio Agnoletti of the Istituto Zooprofilattico Sperimentale del Veneto, Trevise, ItalyBadagliacca, P.; Letizia, A.; Candeloro, L.; Di Provvido, A.; Di Gennaro, A.; Scattolini, S.; Pompei, G.... (2010). Clinical, pathological and microbiological profiles of spontaneous enteropathies in growing rabbits. World Rabbit Science. 18(4):187-198. doi:10.4995/wrs.2010.77518719818

An optimization procedure of the start-up of Combined Cycle Power Plants

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model predictive control tools for evolutionary plants

The analysis and design of control system configurations for automated production systems is generally a challenging problem, in particular given the increasing number of automation devices and the amount of information to be managed. This problem becomes even more complex when the production system is characterized by a fast evolutionary behaviour in terms of tasks to be executed, production volumes, changing priorities, and available resources. Thus, the control solution needs to be optimized on the basis of key performance indicators like flow production, service level, job tardiness, peak of the absorbed electrical power and the total energy consumed by the plant. This paper proposes a prototype control platform based on Model Predictive Control (MPC) that is able to impress to the production system the desired functional behaviour. The platform is structured according to a two-level control architecture. At the lower layer, distributed MPC algorithms control the pieces of equipment in the production system. At the higher layer an MPC coordinator manages the lower level controllers, by taking full advantage of the most recent advances in hybrid control theory, dynamic programming, mixed‐integer optimization, and game theory. The MPC-based control platform will be presented and then applied to the case of a pilot production plant