Pallov Anand and Gholamreza Nazmara – FCT PhD Research Grants 2022

FCT has published the final results of the 2022 Call for Research Grants for Doctoral Research, and 1451 new research grants were recommended for funding.

Two C2SR/SYSTEC researchers secured a place in the pool of funding: Pallov Anand and Gholamreza Nazmara.

An overview of structural systems theory

Guilherme Ramos; A. PedroAguiar; Sérgio Pequito



Abstract(EN): This paper provides an overview of the research conducted in the context of structural (or structured) systems. These are parametrized models used to assess and design system theoretical properties without considering a specific realization of the parameters (which could be uncertain or unknown). The research in structural systems led to a principled approach to a variety of problems, into what is known as structural systems theory. Hereafter, we perform a systematic overview of the problems and methodologies used in structural systems theory since the latest survey by Dion et al. in 2003. During this period, most of the focus seems to be on structural system’s properties related to controllability/observability and decentralized control, in the context of linear time-invariant systems, under the classic assumption that the parameters are independent and belonging to infinite fields. Notwithstanding, it is notable an increase in research in topics that go beyond such scope and underlying assumptions, as well as applications in a variety of domains. Lastly, we provide a compilation of open questions on several settings and we discuss future directions in this field.

Model Predictive Control for Self Driving Cars: A Case Study Using the Simulator CARLA within a ROS Framework

Daniel R Morais, A Pedro Aguiar


DOI: 10.1109/ICARSC55462.2022.9784788

Abstract(EN): Over the past few years, autonomous driving vehicles have been growing rapidly due to advances in technology, namely computing power and improvements in sensors and actuators. This paper presents a research work that addresses the autonomous driving problem. More specifically, a Model Predictive Control (MPC) is implemented for the lateral and longitudinal control of an autonomous vehicle. The main goal of this study is to validate the robustness, performance and safety of the developed algorithm in simulation, using the widely known self-driving simulator CARLA together with the Robot Operating System (ROS) framework. The implemented method provides a realistic simulation and contributes to the area of software development for driving cars offering passengers a more comfortable and safer control of the vehicle.

A Path-Following Controller for Marine Vehicles Using a Two-Scale Inner-Outer Loop Approach

Pramod Maurya, Helio Mitio Morishita, Antonio Pascoal, A. Pedro Aguiar

Sensors (MDPI)


Abstract(EN): This article addresses the problem of path following of marine vehicles along straight lines in the presence of currents by resorting to an inner-outer control loop strategy, with due account for the presence of currents. The inner-outer loop control structures exhibit a fast-slow temporal scale separation that yields simple “rules of thumb” for controller tuning. Stated intuitively, the inner-loop dynamics should be much faster than those of the outer loop. Conceptually, the procedure described has three key advantages: (i) it decouples the design of the inner and outer control loops, (ii) the structure of the outer-loop controller does not require exact knowledge of the vehicle dynamics, and (iii) it provides practitioners a very convenient method to effectively implement path-following controllers on a wide range of vehicles. The path-following controller discussed in this article is designed at the kinematic outer loop that commands the inner loop with the desired heading angles while the vehicle moves at an approximately constant speed. The key underlying idea is to provide a seamless implementation of path-following control algorithms on heterogeneous vehicles, which are often equipped with heading autopilots. To this end, we assume that the heading control system is characterized in terms of an IOS-like relationship without detailed knowledge of vehicle dynamics parameters. This paper quantitatively evaluates the combined inner-outer loop to obtain a relationship for assessing the combined system’s stability. The methods used are based on nonlinear control theory, wherein the cascade and feedback systems of interest are characterized in terms of their IOS properties. We use the IOS small-gain theorem to obtain quantitative relationships for controller tuning that are applicable to a broad range of marine vehicles. Tests with AUVs and one ASV in real-life conditions have shown the efficacy of the path-following control structure developed.

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

Moradzadeh, A.; Moayyed, H.; Mohammadi Ivatloo, B.; Aguiar, AP. ; Anvari Moghaddam, A.

 IEEE Acces

ID Authenticus: P-00V-ZNM

DOI: 10.1109/access.2021.3139529

Abstract (EN): Recently, with the establishment of new thermal regulations, the energy efficiency of buildings has increased significantly, and various deep learning-based methods have been presented to accurately forecast the heating load demand of buildings. However, all of these methods are executed on a dataset with specific distribution and do not have the property of global forecasting, and have no guarantee of data privacy against cyber-attacks. This paper presents a novel approach to heating load demand forecasting based on Cyber-Secure Federated Deep Learning (CSFDL). The suggested CSFDL provides a global super-model for forecasting the heating load demand of different local clients without knowing their location and, most importantly, without revealing their privacy. In this study, a CSFDL global server is trained and tested considering the heating load demand of 10 different clients in their building environment. The presented results, including a comparative study, prove the viability and accuracy of the proposed procedure.

Energy-Optimal Motion Planning for Multiple Robotic Vehicles With Collision Avoidance

Hausler, AJ; Saccon, A; Aguiar, AP; Hauser, J; Pascoal, AM

IEEE Transactions on Control Systems Technology

ID Authenticus: P-00R-YR9

DOI: 10.1109/TCST.2015.2475399

Abstract:We propose a numerical algorithm for multiple-vehicle motion planning that explicitly takes into account the vehicle dynamics, temporal and spatial specifications, and energy-related requirements. As a motivating example, we consider the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) without colliding among themselves and with obstacles, subject to the requirement that the overall energy required for vehicle motion be minimized. With the theoretical setup adopted, the vehicle dynamics are explicitly taken into account at the planning level. This paper formulates the problem of multiple-vehicle motion planning in a rigorous mathematical setting, describes the optimization algorithm used to solve it, and discusses the key implementation details. The efficacy of the method is illustrated through numerical examples for the simultaneous arrival problem. The initial guess to start the optimization procedure is obtained from simple geometrical considerations, e.g., by joining the desired initial and final positions of the vehicles via straight lines. Even though the initial trajectories thus obtained may result in intervehicle and vehicle/obstacle collisions, we show that the optimization procedure that we employ in this paper will generate collision-free trajectories that also minimize the overall energy spent by each vehicle and meet the required temporal and spatial constraints. The method developed applies to a very general class of vehicles; however, for clarity of exposition, we adopt as an illustrative example the case of wheeled robots.

Second-Order-Optimal Minimum-Energy Filters on Lie Groups

Saccon, A; Trumpf, J; Mahony, R; Aguiar, AP

IEEE Transactions on Automatic Control

ID Authenticus: P-00M-57F

DOI: 10.1109/tac.2015.2506662

Abstract: Systems on Lie groups naturally appear as models for physical systems with full symmetry. We consider the state estimation problem for such systems where both input and output measurements are corrupted by unknown disturbances. We provide an explicit formula for the second-order-optimal nonlinear filter on a general Lie group where optimality is with respect to a deterministic cost measuring the cumulative energy in the unknown system disturbances (minimum-energy filtering). The resulting filter depends on the choice of affine connection which encodes the nonlinear geometry of the state space. As an example, we look at attitude estimation, where we are given a second order mechanical system on the tangent bundle of the special orthogonal group SO(3), namely the rigid body kinematics together with the Euler equation. When we choose the symmetric Cartan-Schouten (0)-connection, the resulting filter has the familiar form of a gradient observer combined with a perturbed matrix Riccati differential equation that updates the filter gain. This example demonstrates how to construct a matrix representation of the abstract general filter formula.

Moving Path Following for Unmanned Aerial Vehicles With Applications to Single and Multiple Target Tracking Problems

Oliveira, T; Aguiar, AP; Encarnacao, P

IEEE Transactions on Robotics

ID Authenticus: P-00K-VS8

DOI: 10.1109/tro.2016.2593044

Abstract: This paper introduces the moving path following (MPF) problem, in which a vehicle is required to converge to and follow a desired geometric moving path, without a specific temporal specification, thus generalizing the classical path following that only applies to stationary paths. Possible tasks that can be formulated as an MPF problem include tracking terrain/air vehicles and gas clouds monitoring, where the velocity of the target vehicle or cloud specifies the motion of the desired path. We derive an error space for MPF for the general case of time-varying paths in a two-dimensional space and subsequently an application is described for the problem of tracking single and multiple targets on the ground using an unmanned aerial vehicle (UAV) flying at constant altitude. To this end, a Lyapunov-based MPF control law and a path-generation algorithm are proposed together with convergence and performance metric results. Real-world flight tests results that took place in Ota Air Base, Portugal, with the ANTEX-X02 UAV demonstrate the effectiveness of the proposed method.

Safe Coordinated Maneuvering of Teams of Multirotor Unmanned Aerial Vehicles: A Cooperative Control Framework For Multivehicle, Time-Critical Missions

Cichella, V; Choe, R; Mehdi, SB; Xargay, E; Hovakimyan, N; Dobrokhodov, V; Kaminer, I; Pascoal, AM; Aguiar, AP


ID Authenticus: P-00K-SAC

DOI: 10.1109/mcs.2016.2558443

Abstract: Multirotor unmanned aerial vehicles (UAVs) have experienced a very fast-paced technological development over the past years. Flight control systems have evolved from simple stability augmentation systems, barely enabling an external pilot to remotely fly a multirotor UAV, to full-fledged command augmentation systems, opening up the possibilities of autonomous operations of multirotors. Due to their small size, low cost, and high agility, multirotors draw a plethora of applications, including multiple vehicles operations, where the vehicles cooperate and jointly execute a mission, in constrained complex environments such as crowd monitoring and utility line inspection.