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About

About

I was born in Oporto, Portugal, in 1958. I was graduatedi n Electrical Engineering in 1981 at Opoto University, and received the MSc degree in Computers and Digital Systems in 1987, the Ph.D. degree in Electrical Engineering in 1994 and the Agregado degree in 2016, all from
Oporto University. From 1983 to 1994 I worked as an assistant lecturer in the Electrical Engineering Department of the Oporto University. In 1985 I started a full-time academic career, and presently I am a Lecturer in Electrical Engineering at the same University.

From 1985 to 1989  I developed his research in INESC. I moved d to the Institute for Systems and Robotics- Oporto (ISRP) in 1989 where I stayed until 2018. I have joined the INESC TEC in 2018.

My research interests are Control, Estimation, Dynamical Systems Identification including multi-dimensional systems, with applications ranging from Bimedical Systems to Energy Systems.

I am author and co-author of dozens of papers published in international journals and proceedings of international conferences. I am a member of the the Portuguese Association of Automatic Control (APCA), IEEE CST and of the IEEE CST International Technical Committee on Systems Identification and Adaptive Control,  the IEEE CST International Technical Committee on Health and Medical Systems TC  and of the IFAC (International Federation on Automatic Control) Technical Committee on Signal Processing.

Interest
Topics
Details

Details

003
Publications

2023

Modeling and Identification of Li-ion Cells

Authors
dos Santos, PL; Perdicoulis, TPA; Salgado, PA;

Publication
IEEE CONTROL SYSTEMS LETTERS

Abstract
To develop a full battery model in view to accurate battery management, Li-ion cell dynamics is modelled by a capacitor in series with a simplified Randles circuit. The open circuit voltage is the voltage at the capacitor terminals, allowing, in this way, for the dependence of the open circuit voltage on the state-of-charge to be embedded in its capacitance. The Randles circuit is recognised as a trusty description of a cell dynamics. It contains a semi-integrator of the current, known as the Warburg impedance, that is a special case of a fractional integrator. To enable the formulation of a time-domain system identification algorithm, the Warburg impedance impulse response was calculated and normalised, in order to derive a finite order state-space approximation, using the Ho-Kalman algorithm. Thus, this Warburg impedance LTI model, with known parameters (normalised impedance) in series with a gain block, is suitable for system identification, since it has only one unknown parameter. A LTI System identification Algorithm was formulated to estimate the model parameters and the initial values of both the open circuit voltage and the states of the normalised Warburg impedance. The performance of the algorithm was very satisfactory on the whole state-of-charge region and when compared with low order Thevenin models. Once it is understood the parameters variability on the state-of-charge, temperature and ageing, we envisage to continue the work using parameter-varying algorithms.

2022

Editorial: Linear Parameter Varying Systems Modeling, Identification and Control

Authors
Lopes Dos Santos, P; Azevedo Perdicoulis, T; Ramos, JA; Fontes, FACC; Sename, O;

Publication
Frontiers in Control Engineering

Abstract

2022

Energy loss optimisation of a robotic arm

Authors
Salgado, PA; Perdicoulis, TPA; dos Santos, PL;

Publication
2022 IEEE 22ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS AND 8TH IEEE INTERNATIONAL CONFERENCE ON RECENT ACHIEVEMENTS IN MECHATRONICS, AUTOMATION, COMPUTER SCIENCE AND ROBOTICS (CINTI-MACRO)

Abstract
The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab (R) fminunc function and the Particle Swam Optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.

2021

A Non-Parametric LPV Approach to the Indentification of Linear Periodic Systems

Authors
dos Santos, PL; Perdicoulis, TPA;

Publication
IFAC PAPERSONLINE

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.

2020

System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

Authors
dos Santos, PL; Freigoun, MT; Martin, CA; Rivera, DE; Hekler, EB; Romano, RA; Azevedo Perdicoulis, TPA;

Publication
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

Abstract
System identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies.

Supervised
thesis

2018

Identificação de Sistemas Utilizando a Parametrização MOLI

Author
Patrícia Gomes Saraiva

Institution
UP-FEUP