Estudo comparativo entre técnicas de controle lineares e não-lineares implementadas em FPGA aplicadas a um inversor de tensão NPC três níveis monofásicos

This work presents a comparative study, design and implementation of linear and nonlinear control techniques applied on power electronics converters. The main objective of this study is to control the output voltage and the voltage balance between the DC bus voltage capacitors balance of a three level NPC inverter. The control techniques used are the classic PID, the ANLPID-GGF, LQR and SDRE. The Non-Linear PID – Gaussian Gain Functions is considered a new nonlinear control technique used for optimization of the classic linear PID control. Taken the converter transfer function, the designs of the four controllers are set so the operating point of the closed loop system presents the same natural frequency. Thus, the comparative analysis of the performance of each control can be performed more precisely. The pole placement technique is used to design the LQR and SDRE controllers. By means of a comparative analysis of the controllers, it a parallel combination of the designed controllers is also proposed, yielding a weighted adaptive control system. The weighted adaptive controller improves the system performance, both in response time and in controlled magnitudes overshoots reduction. The four control techniques and the strategy an adaptive control proposed are implemented on FPGA, using the DSP Builder tool for the development and compilation the VHDL code. Simulation results are presented in order to validate the proposed theoretical development.Este trabalho apresenta um estudo comparativo, o projeto e a implementação de técnicas de controle lineares e não-lineares aplicadas em conversores estáticos de energia elétrica. O principal objetivo deste estudo é controlar, tanto a tensão de saída como o equilíbrio das tensões dos capacitores do barramento CC (Corrente Contínua) de um inversor NPC (Neutral Point Campled) três níveis monofásico. As técnicas de controle utilizadas são o PID (Proporcional-Integral-Derivativo), o ANLPID-GGF (Adaptive Non Linear PID – Gaussian Like Gain Functions), o LQR (Linear Quadratic Regulator) e o SDRE (State Dependent Riccati Equation). O ANLPID-GGF é uma proposta de otimização do controlador PID convencional utilizando funções de ganhos variáveis. Todos os controladores são projetados para manter os mesmos pólos dominantes (autovalores) do sistema em malha fechada de acordo com o modelo do conversor. Desta forma é possível realizar uma comparação mais coerente entre as quatro técnicas de controles estudadas. Com a determinação dos pólos dominantes do sistema é realizado o projeto dos controladores LQR e SDRE por alocação de pólos. Com o projeto por alocação de pólos pode-se evitar os sobre sinais comuns nas aplicações que utilizam controladores LQR e SDRE. Através da análise comparativa entre os quatro controladores, pode-se identificar as características distintas de cada método. Desta forma, também é proposta uma implementação paralela de controladores onde é feita uma associação ponderada entre as leis de controle, dando origem a uma estratégia de controle adaptativo ponderado que melhora o desempenho do sistema, tanto em tempo de resposta quanto na redução dos overshoots. As quatro técnicas de controle utilizadas e a estratégia proposta do controle adaptativo são implementadas em FPGA (Field Programmable Gate Array), utilizando a ferramenta DSP Builder para desenvolvimento e compilação do código HDL (Linguagem de Descrição de Hardware). Resultados de simulações e experimentais são apresentados com o objetivo de validar o desenvolvimento teórico proposto

A Hyperbolic Tangent Adaptive PID + LQR Control Applied to a Step-Down Converter Using Poles Placement Design Implemented in FPGA

This work presents an adaptive control that integrates two linear control strategies applied to a step-down converter: Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controls. Considering the converter open loop transfer function and using the poles placement technique, the designs of the two controllers are set so that the operating point of the closed loop system presents the same natural frequency. With poles placement design, the overshoot problems of the LQR controller are avoided. To achieve the best performance of each controller, a hyperbolic tangent weight function is applied. The limits of the hyperbolic tangent function are defined based on the system error range. Simulation results using the Altera DSP Builder software in a MATLAB/SIMULINK environment of the proposed control schemes are presented

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost