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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.

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Publications

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. IEEE

2019

A Dynamic Mode Decomposition Approach with Hankel Blocks to Forecast Multi-Channel Temporal Series

Authors
Filho, EV; Dos Santos, PL;

Publication
IEEE Control Systems Letters

Abstract
Forecasting is a task with many concerns, such as the size, quality, and behavior of the data, the computing power to do it, etc. This letter proposes the dynamic mode decomposition (DMD) as a tool to predict the annual air temperature and the sales of a stores' chain. The DMD decomposes the data into its principal modes, which are estimated from a training data set. It is assumed that the data is generated by a linear time-invariant high order autonomous system. These modes are useful to find the way the system behaves and to predict its future states, without using all the available data, even in a noisy environment. The Hankel block allows the estimation of hidden oscillatory modes, by increasing the order of the underlying dynamical system. The proposed method was tested in a case study consisting of the long term prediction of the weekly sales of a chain of stores. The performance assessment was based on the best fit percentage index. The proposed method is compared with three neural network-based predictors. © 2017 IEEE.

2019

A Kernel Principal Component Regressor for LPV System Identification

Authors
dos Santos, PL; Perdicoulis, TPA;

Publication
IFAC PAPERSONLINE

Abstract
This article describes a Kernel Principal Component Regressor (KPCR) to identify Auto Regressive eXogenous (ARX) Linear Parmeter Varying (LPV) models. The new method differs from the Least Squares Support Vector Machines (LS-SVM) algorithm in the regularisation of the Least Squares (LS) problem, since the KPCR only keeps the principal components of the Gram matrix while LS-SVM performs the inversion of the same matrix after adding a regularisation factor. Also, in this new approach, the LS problem is formulated in the primal space but it ends up being solved in the dual space overcoming the fact that the regressors are unknown. The method is assessed and compared to the LS-SVM approach through 2 Monte Carlo (MC) experiments. Every experiment consists of 100 runs of a simulated example, and a different noise level is used in each experiment,with Signal to Noise Ratios of 20db and 10db, respectively. The obtained results are twofold, first the performance of the new method is comparable to the LS-SVM, for both noise levels, although the required calculations are much faster for the KPCR. Second, this new method reduces the dimension of the primal space and may convey a way of knowing the number of basis functions required in the Kernel. Furthermore, having a structure very similar to LS-SVM makes it possible to use this method in other types of models, e.g. the LPV state-space model identification.

2018

The secrets of Segway revealed to students: revisiting the inverted pendulum

Authors
Azevedo Perdicoulis, TPA; Lopes dos Santos, PL;

Publication
2018 13TH APCA INTERNATIONAL CONFERENCE ON CONTROL AND SOFT COMPUTING (CONTROLO)

Abstract
This article revisits the inverted pendulum-in particular, analyses a simplified model of a Segway, with a view to exploring its capabilities in Control Systems Engineering education. The integration between the theoretic and practical side is achieved through simulation, and in particular by using MathWorks software. We also present a structure for the work to be done in the Laboratory class and propose a solution for the problem.

2018

An extended instrument variable approach for nonparametric LPV model identification

Authors
Lima, MML; Romano, RA; dos Santos, PL; Pait, F;

Publication
IFAC PAPERSONLINE

Abstract
Linear parameter varying models (LPV) have proven to be effective to describe non-linearities and time-varying behaviors. In this work, a new non-parametric estimation algorithm for state-space LPV models based on support vector machines is presented. This technique allows the functional dependence between the model coefficients and the scheduling signal to be "learned" from the input and output data. The proposed algorithm is formulated in the context of instrumental (IV) estimators, in order to obtain consistent estimates for general noise conditions. The method is based on a canonical state space representation and admits a predictor form that has shown to be suitable for system identification, as it leads to a convenient regression form. In addition, this predictor has an inherent filtering feature. In the context of vector support machines, such filtering mechanism leads to two-dimensional data processing, which can be used to decrease the variance of estimates due to noisy data. The performance of the proposed approach is evaluated from simulated data subject to different noise scenarios. The technique was able to reduce the error due to the variance of the estimator in most of the analyzed scenarios.