A hierarchical MPC scheme for interconnected systems

This paper describes a hierarchical control scheme for interconnected systems. The higher layer of the control structure is designed with robust Model Predictive Control (MPC) based on a reduced order dynamic model of the overall system and is aimed at optimizing long-term performance, while at the lower layer local regulators acting at a higher frequency are designed for the full order models of the subsystems to refine the control action. A simulation experiment concerning the control of the temperature inside a building is reported to witness the potentialities of the proposed approach

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.Comment: Accepted for Learning for dynamics & control (L4DC) 202

Black magnetic spherules from the glacial and sea ice of Fletcher\u27s Ice Island (T-3)

Black magnetic spherules are particles which can be derived by ablationary processes from cosmic sources. Spherules from both the glacial ice and sea ice of Fletcher\u27s Ice Island (T-3) were studied to determine their size distribution, sedimentation rates, and other parameters. The results were examined and shown to be similar to those of other researchers\u27 work elsewhere. Calculated sedimentation rates for glacial ice spherules, extrapolated for the entire earth\u27s surface, range from 1.1 x 10⁴ to 1.1 x 10⁵ metric tons per year. Calculated sedimentation rates for sea ice spherules range from 5.0 x 10³ to 1.6 x 10⁵ metric tons per year. Vertical variations in cumulative mass for closely spaced glacial ice cores indicate a similarity of depositional pattern. This study represents the first known occurrence of spherules in Arctic sea ice but, does not explain the mechanism by which such particles are included. Any acceptable interpretation requires an explanation of the presence of spherules at depth in the sea ice

Learning-based predictive control for linear systems: a unitary approach

A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p-steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is derived, to be used for robust constraint satisfaction. Resorting to Set Membership techniques, a characterization of the bounded model uncertainties is obtained, which is a key feature for a successful application of the robust control algorithm. In the control design phase, a robust MPC law is proposed, able to track piece-wise constant reference signals, with guaranteed recursive feasibility and convergence properties. The controller embeds multistep predictors in the cost function, it ensures robust constraints satisfaction thanks to the learnt uncertainty model, and it can deal with possibly unfeasible reference values. The proposed approach is finally tested in a numerical example

Un límite al enfoque enactivo: la percepción del valor

La concepción enactiva de la percepción que Alva Noe defiende en Action in Perception resalta el papel que desempeñan la acción y las habilidades prácticas del sujeto en la constitución de la experiencia perceptual. Para Noe las habilidades sensoriomotoras juegan un papel constitutivo del contenido de la experiencia perceptual, ya que son entendidas como los conceptos que hacen inteligible lo que la percepción presenta. La tesis de este trabajo es que Noe no muestra que dichas habilidades sensoriomotoras constituyan enteramente el contenido conceptual de la percepción, puesto que no explica adecuadamente cómo experimentamos perceptualmente el valor instrumental de los objetos de nuestro entorno. En la primera sección esbozo la teoría de la percepción defendida por Noe. En la segunda sección presento la limitación antes mencionada, argumentando que Noe equipara erróneamente la percepción de objetos como instrumentalmente valiosos con la percepción de posibilidades de movimiento. En la tercera sección, finalmente, adelanto una caracterización de la percepción de objetos como valiosos que explica por qué no es conveniente realizar tal equiparación

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 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

Stability of discrete-time feed-forward neural networks in NARX configuration

The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named Neural NARXs (NNARXs), has been quite popular in the early days of machine learning applied to nonlinear system identification, owing to their simple structure and ease of application to control design. Nonetheless, few theoretical results are available concerning the stability properties of these models. In this paper we address this problem, providing a sufficient condition under which NNARX models are guaranteed to enjoy the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS) properties. This condition, which is an inequality on the weights of the underlying FFNN, can be enforced during the training procedure to ensure the stability of the model. The proposed model, along with this stability condition, are tested on the pH neutralization process benchmark, showing satisfactory results.Comment: This work has been submitted to IFAC for possible publicatio