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Publications

Publications by Tatiana Martins Pinho

2017

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Authors
Pinho, TM; Coelho, JP; Oliveira, J; Boaventura Cunha, J;

Publication
JOURNAL OF SENSORS

Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is amajor concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

2017

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

Authors
Pinho, TM; Coelho, JP; Veiga, G; Paulo Moreira, AP; Boaventura Cunha, J;

Publication
COMPLEXITY

Abstract
Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.

2017

Model predictive control applied to a supply chain management problem

Authors
Pinho, TM; Coelho, JP; Moreira, AP; Boaventura Cunha, J;

Publication
Lecture Notes in Electrical Engineering

Abstract
Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017.

2017

Model predictive control of a conveyor-based drying process applied to cork stoppers

Authors
Tavares, P; Pinho, TM; Boaventura Cunha, J; Moreira, AP;

Publication
Lecture Notes in Electrical Engineering

Abstract
Control applications are a key aspect of current industrial environments. Regarding cork industries, there is a particular process that needs to be addressed: the cork stoppers drying. Currently the methodology used in this process delays the overall production cycle and lacks in the drying efficiency itself. This paper presents the development of a cork stopper drying system based on the control of a conveyor based machine using Model Predictive Control (MPC). Throughout the project itwas also developed a drying kineticsmodel for the cork stoppers and an extension of such model to a discrete space state model. By applying the proposed methodology it is assured the cork stoppers’ drying in a faster and more efficient way. © Springer International Publishing Switzerland 2017.

2017

Swarm-based Auto-tuning of PID Posicast Control for Uncertain Systems

Authors
Oliveira, J; Oliveira, PM; Pinho, TM; Boaventura Cunha, J;

Publication
2017 25TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED)

Abstract
Posicast feedback control systems are very sensitive to model uncertainty. This paper proposes the use of Particle Swarm Optimization (PSO) to auto-tune two-degrees of freedom control systems. The system considers as a pre-filter a half-cycle Posicast command shaper and a PID controller in the feedback loop. A model reference technique is proposed to track differences among model and system to be controlled, feeding a decision block which will trigger an auto-tuning optimization mechanism. Preliminary simulation results are presented showing the proposed technique effectiveness to deal with prescribed plant uncertainties.

2017

Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator

Authors
Oliveira, J; Oliveira, PM; Boaventura Cunha, J; Pinho, T;

Publication
NONLINEAR DYNAMICS

Abstract
The use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal.

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