Search results for "state space"
showing 10 items of 49 documents
Modelling and Analysis of Flow Rate and Pressure Head in Pipelines
2019
Currently, various approaches with several utilities are proposed to identify damage in the pipeline. The pipeline system is modeled in the form of a distributed parameter system, such that the state space related to the distributed parameter system contains infinite dimension. In this paper, a novel technique is proposed to analyze and model the flow in the pipeline. Important theorems are proposed for testing the observability as well as controllability of the proposed model.
Pipeline Monitoring Architecture Based on Observability and Controllability Analysis
2019
Recently many techniques with different applicability have been developed for damage detection in the pipeline. The pipeline system is designed as a distributed parameter system, where the state space of the distributed parameter system has infinite dimension. This paper is dedicated to the problem of observability as well as controllability analysis in the pipeline systems. Some theorems are presented in order to test the observability and controllability of the system. Computing the rank of the controllability and observability matrix is carried out using Matlab.
Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension
2016
We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For th…
Exact solution of the soft-clustered vehicle-routing problem
2020
Abstract The soft-clustered vehicle-routing problem (SoftCluVRP) extends the classical capacitated vehicle-routing problem by one additional constraint: The customers are partitioned into clusters and feasible routes must respect the soft-cluster constraint, that is, all customers of the same cluster must be served by the same vehicle. In this article, we design and analyze different branch-and-price algorithms for the exact solution of the SoftCluVRP. The algorithms differ in the way the column-generation subproblem, a variant of the shortest-path problem with resource constraints (SPPRC), is solved. The standard approach for SPPRCs is based on dynamic-programming labeling algorithms. We s…
Learning Automata-based Misinformation Mitigation via Hawkes Processes
2021
AbstractMitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint ra…
Multi-agent Reinforcement Learning for Simulating Pedestrian Navigation
2012
In this paper we introduce a Multi-agent system that uses Reinforcement Learning (RL) techniques to learn local navigational behaviors to simulate virtual pedestrian groups. The aim of the paper is to study empirically the validity of RL to learn agent-based navigation controllers and their transfer capabilities when they are used in simulation environments with a higher number of agents than in the learned scenario. Two RL algorithms which use Vector Quantization (VQ) as the generalization method for the space state are presented. Both strategies are focused on obtaining a good vector quantizier that generalizes adequately the state space of the agents. We empirically state the convergence…
State Space-Vector Model of Linear Induction Motors Including End-effects and Iron Losses - Part II: Model Identification and Results
2020
This is the second part of an article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost …
State Space-Vector Model of Linear Induction Motors including End-Effects and Iron Losses Part I: Theoretical Analysis
2020
This is the first part of the article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses and its offline identification. This first part specifically treats the theoretical formulation of this model, which has been expressed in a state form, so to be, in perspective, suitably adopted for developing novel nonlinear control techniques, nonlinear observers as well as electrical losses minimization techniques. Besides the formulation of the dynamic model in space-vector state form, a steady-state analysis is proposed, highlighting the combined effects …
Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes
2019
Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …
A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes
2021
The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It p…