Predictor Design for Altitude Control of a Seaweed Harvester

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010)

Predictor Design for Altitude Control of a Seaweed Harvester

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010)

Modeling, estimation and identification of complex system dynamics: issues and solutions

La modellazione dei sistemi è di fondamentale importanza in tutte le discipline, sono utili per l’analisi, la previsione o la simulazione dei sistemi. Esistono due pratiche per definire modelli: modellazione e di identificazione. La modellazione è basata su leggi note. L’identificazione consiste nella selezione di un modello sulla base delle osservazioni effettuate sul sistema. In questo lavoro si è dato un contributo all’identificazione e stima di dinamiche complesse di sistemi. Con attenzione ai sistemi reali, sono proposte tre soluzioni. Il primo argomento riguarda un inceneritore per rifiuti solidi urbani, dove i modelli matematici sono troppo complessi per essere utilizzati. La soluzione data è in grado di stimare e predire, la produzione di vapore di un inceneritore RSU. L’algoritmo di apprendimento si basa su reti di funzioni a base radiale e combina la tecnica Minimal Resource Allocating Network con un filtro di Kalman esteso adattativo per aggiornare i parametri della rete. Il secondo problema riguarda la compensazione degli errori di controllo per un manipolatore industriale. Se un contro è ben progettato l’errore di controllo non può essere compensato. Tuttavia nel controllo Sliding Mode discreto, l’errore di controllo presenta dinamiche residue. Si propongono sue approcci per compensare l’incertezza, l’obiettivo è sviluppare un SMC discreto più robusto con due soluzioni, una basata sullo stimatore di incertezza del modello, e un predittore autosintonizzante. La diagnosi guasti ha ricevuto un crescente interesse degli ultimi anni. L’ultimo argomento riguarda una procedura di rilevamento guasti e isolamento per la rilevazione e l’analisi di difetti di motori elettrici a fine linea di un impianto di produzione di cappe. L’obiettivo consiste nel rilevare e identificare i motori difettosi per l’analisi di qualità. Un approccio diagnostico basato sull’analisi dei segnali è preferibile per le caratteristiche dei segnali acquisiti e per la soluzione di implementazione

RGBD camera monitoring system for Alzheimer’s disease assessment using Recurrent Neural Networks with Parametric Bias action recognition

The present paper proposes a computer vision system to diagnose the stage of illness in patients a ected by Alzheimer's disease. In the context of Ambient Assisted Living (AAL), the system monitors people in home environment during daily personal care activities. The aim is to evaluate the dementia stage, observing actions listed in the Direct Assessment of Funcional Status (DAFS) index and detecting anomalies during the performance, in order to assign a score explaining if the action is correct or not. In this work brushing teeth and grooming hair by a hairbrush are analysed. The technology consists of the application of a Recurrent Neural Network with Parametric Bias (RNNPB) that is able to learn movements connected with a speci c action and recognize human activities by parametric bias that work like mirror neurons. This study has been conducted using Microsoft Kinect to collect data about the actions observed and oversee the user tracking and gesture recognition. Experiments prove that the proposed computer vision system can learn and recognize complex human activities and evaluates DAFS score

Predictor Design for Altitude Control of a Seaweed Harvester

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010)

Predictor Design for Altitude Control of a Seaweed Harvester

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010)

Electric motor defects diagnosis based on kernel density estimation and Kullback-Leibler divergence in quality control scenario

The present paper deals with the defect detection and diagnosis of induction motor, based on motor current signature analysis in a quality control scenario. In order to develop a monitoring system and improve the reliability of induction motors, Clarke-Concordia transformation and kernel density estimation are employed to estimate the probability density function of data related to healthy and faulty motors. Kullback-Leibler divergence identifies the dissimilarity between two probability distributions and it is used as an index for the automatic defects identification. Kernel density estimation is improved by fast Gaussian transform. Since these techniques achieve a remarkable computational cost reduction respect the standard kernel density estimation, the developed monitoring procedure became applicable on line, as a Quality Control method for the end of production line test. Several simulations and experimentations are carried out in order to verify the proposed methodology effectiveness: broken rotor bars and connectors are simulated, while experimentations are carried out on real motors at the end of production line. Results show that the proposed data-driven diagnosis procedure is able to detect and diagnose different induction motor faults and defects, improving the reliability of induction machines in quality control scenario. © 2015 Elsevier Ltd