Design and real-time implementation of a wireless autopilot using multivariable predictive generalized minimum variance control in the state-space

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

Sodré

Silva

et al. 2021

Assuming an imminent futuristic scenario, of 5G-and-Beyond communications networks, where different classes of autonomous unmanned aerial vehicles will use guidance, navigation and control systems relying on the network's performance, in this paper we investigate the delay and packet loss effects which may lead to closed-loop stability margins degradation of such aerial vehicles, causing accidents. The use of state observers can minimize these adverse effects by allowing these control systems to receive estimated data whenever sensor data is delayed or lost. In this sense, we propose the assessment of a Smith predictor-based selftuning control applied to a 6-DOF model of a network-controlled quadrotor. The investigation is focused on Proportional-Integral-Derivative control due to its solid acceptance as a trustworthy industrial technique. Simulations and robustness indices indicates that the investigated control approach can guarantee the robust stability of such aerial systems within the considered scenario.

Section: Simulations Resultsmentioning

confidence: 99%

Smith Predictor-based Adaptive Control of Network-Controlled UAVs

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

Sodré

Silva

et al. 2021

“…LSA-based estimation is becoming more popular in industrial settings, such as in single-input, single-output (SISO) systems and in MIMO systems; in linear and non-linear system identification; and in polynomial and state-space MIMO system realizations [58,59]. Such an increase in the use of RLS for realtime applications is justified by the advances of microprocessors to cope with the time constraints of fast dynamical systems, where the sampling interval can go as short as a few nanoseconds.…”

Section: Discussionmentioning

confidence: 99%

Estimation and Monitoring of COVID-19's Transmissibility From Publicly Available Data

Front. Appl. Math. Stat.

Pereira

2020

Self Cite

The 2019 coronavirus disease (COVID-19) pandemic began in the city of Wuhan, China, at the end of 2019 and quickly spread worldwide. The disease is caused by contact with the SARS-CoV-2 virus, which probably jumped from an animal host to humans. SARS-CoV-2 infects various tissues in the body, notably the lungs, and patients usually die from respiratory complications. Mathematical models of the disease have been instrumental to guide the implementation of mitigation strategies aimed at slowing the spread of the disease. One of the key parameters of mathematical models is the basic reproduction ratio R 0 , which measures the degree of infectivity of affected individuals. The goal of mitigation is to reduce R 0 as close or below 1 as possible, as it means that new infections are in decline. In this work, we use the recursive least-squares algorithm to establish the stochastic variability of a time-varying R 0 (t) from eight different countries:

“…LSA-based estimation is becoming more popular in industrial settings, such as: in single-input, single-output systems (SISO) and in multiple-inputs, multiple-outputs systems (MIMO); in linear and non-linear system identification, and in polynomial and state-space MIMO system realizations [59, 60]. Such an increase in the use of RLS for real-time applications is justified by the advances of microprocessors to cope with the time constraints of fast dynamical systems, where the sampling interval can go as short as a few nanoseconds.…”

Section: Discussionmentioning

confidence: 99%

Estimation and monitoring of COVID-19’s transmissibility from publicly available data

Pereira

2020

Preprint

Self Cite

The COVID-19 pandemic began in the city of Wuhan, China, at the end of 2019 and quickly spread worldwide. The disease is caused by contact with the SARS-CoV-2 virus, which probably jumped from an animal host to humans. SARS-CoV-2 infects various tissues in the body, notably the lungs, and patients usually die from respiratory complications. Mathematical models of the disease have been instrumental to guide the implementation of mitigation strategies aimed at slowing the spread of the disease. One of the key parameters of mathematical models is the basic reproduction ratio R0, which measures the degree of infectivity of affected individuals. The goal of mitigation is to reduce R0 as close or below 1 as possible, as it means that new infections are in decline. In the present work, we use the recursive least-squares algorithm to establish the stochastic variability of a time-varying R0(t) from eight different countries: Argentina, Belgium, Brazil, Germany, Italy, New Zealand, Spain, and the United States of America. The proposed system can be implemented as an online tracking application providing information about the dynamics of the pandemic to health officials and the public at large.