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About

Computer engineer with post-doctoral experience in Electrical Engineering at University of Trás-os-Montes and Alto Douro (UTAD, Portugal, 2013) and PhD degree in Automation and Systems from Federal University of Rio Grande do Norte (UFRN), Brazil, in 2007. Professor of Computing/Automation in Brazil (UFRN) since 2003, where has a 13-year experience coordinating academic courses and human resources. Besides, he leaders the electronics laboratory and the research group on Modelling, Instrumentation and Control of Agricultural Systems, where he coordinated/participated in several research projects on technologies of computing and automation applied to agricultural sciences (control systems, embedded systems, digital image processing). Presently, he is a senior researcher at INESC TEC, Portugal. Member of the Brazilian Society of Automation (SBA) and member of the technical commitee of International Federation of Automatic Control (IFAC) on Modelling and Control of Environmental Systems, and Control in Agriculture. Currently is the scientific leader of the ERANET Water JPI project AgriSensus.

Interest
Topics
Details

Details

  • Name

    Josenalde Barbosa Oliveira
  • Cluster

    Industry and Innovation
  • Role

    External Research Collaborator
  • Since

    01st January 2016
Publications

2018

Trends in Gravitational Search Algorithm

Authors
de Moura Oliveira, PBD; Oliveira, J; Cunha, JB;

Publication
Distributed Computing and Artificial Intelligence, 14th International Conference, DCAI 2017, Porto, Portugal, 21-23 June, 2017

Abstract
The gravitational search algorithm (GSA) is reviewed, by presenting a tutorial analysis of its key issues. As any other metaheuristic, GSA requires the selection of some heuristic parameters. One parameter which is crucial in regulating the exploratory capabilities of this algorithm is the gravitational constant. An analysis regarding this parameter selection is presented and a heuristic rule proposed for this purpose. The GSA performance is compared both with a hybridization with particle swarm optimization (PSO) and standard PSO. Preliminary simulation results are presented considering simple continuous functions optimization examples. © Springer International Publishing AG 2018.

2018

PID Posicast Control for Uncertain Oscillatory Systems: A Practical Experiment

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

Publication
IFAC-PapersOnLine

Abstract

2018

A Sliding Mode-Based Predictive Strategy for Irrigation Canal Pools

Authors
Oliveira, J; Pinho, TM; Coelho, J; Boaventura-Cunha, J; Moura Oliveira, P;

Publication

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.

2018

Posicast based experiments to motivate undergraduates to control engineering

Authors
Vidal, S; Oliveira, PM; Oliveira, J; Pinho, T; Cunha, JB;

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
Motivating undergraduate engineering students for the area of control engineering can be a challenging task. Posicast control can be used as a simple technique to introduce both open-loop and closed-loop control systems. This paper addresses several approaches to teach Posicast control involving simulation and practical implementations. A demonstration experiment using the robotic arm (UR5) is reported here as an alternative practical system which can be used to demonstrate Posicast Control. Results obtained from student's perceptions of the reported experiment are presented. © 2018 IEEE.

2018

An overview on visual sensing for automatic control on smart farming and forest management

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

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
This work presents the state-of-the-art of visual sensing systems for monitoring and control purposes in both agriculture and forest areas. Regarding agricultural activities, four main topics are explored: robotics and autonomous vehicles, plant protection, feature extraction and yield prediction. Although vast literature can be found on image processing and computer vision applied to agriculture, its applications in forest-based systems are less frequent. Throughout this article, several research areas such as diseases control, post-processing, parameters estimation, UAVs and satellites will be addressed. © 2018 IEEE.