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Publications

Publications by Nuno Miguel Ferreira

2012

Introducing the fractional-order Darwinian PSO

Authors
Couceiro, MS; Rocha, RP; Fonseca Ferreira, NMF; Tenreiro Machado, JAT;

Publication
SIGNAL IMAGE AND VIDEO PROCESSING

Abstract
One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machine-learning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed. Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO.

2012

An efficient method for segmentation of images based on fractional calculus and natural selection

Authors
Ghamisi, P; Couceiro, MS; Benediktsson, JA; Ferreira, NMF;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures.

2012

A fuzzified systematic adjustment of the robotic Darwinian PSO

Authors
Couceiro, MS; Tenreiro Machado, JAT; Rocha, RP; Ferreira, NMF;

Publication
ROBOTICS AND AUTONOMOUS SYSTEMS

Abstract
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

2003

Fractional-order hybrid control of robotic manipulators

Authors
Ferreira, NMF; Machado, JAT;

Publication
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS 2003, VOL 1-3

Abstract
This paper presents the implementation of fractional-order algorithms in the position/force hybrid control of robotic manipulators. The system performance and robustness is analyzed in the time and frequency domains. The effect of dynamic backlash and flexibility is also investigated.

2010

Electric vehicle drive system with adaptive PID control

Authors
Gouceiro, MS; Figueiredo, CM; Lebres, C; Ferreira, NMF; MacHado, JAT;

Publication
Proceedings of the IASTED International Conference on Modelling, Identification and Control

Abstract
The aim of this work is to implement an adaptive PID SISO feedback control to obtain a fine adjustment of an electric vehicle (EV) driving system. Our research work is done under different operating conditions, namely, variable battery voltage and variable load. A comparison between conventional and adaptive PID algorithms is established when they are applied to the above mentioned conditions. Experimental results indicate that the adaptive PID controller leads to a faster response and a better stability. Furthermore, the adaptive PID controller follows a given reference velocity faster and more smoothly than the conventional PID controller.

2009

Two Cooperating Manipulators with Fractional Controllers

Authors
Fonseca Ferreira, NMF; Tenreiro Machado, JAT; Tar, JK;

Publication
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS

Abstract
This paper analyzes the dynamic performance of two cooperative robot manipulators. It is studied the implementation of fractional-order algorithms in the position/force control of two cooperating robotic manipulators holding an object. The simulations reveal that fractional algorithms lead to performances superior to classical integer-order controllers.

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