Linear Parameter Varying Approaches as Advanced Control Techniques: Application to Vehicle Dynamics

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Ce travail de Fin-d’études présente plusieurs techniques de modélisation, identification et de la commande avancée appliqués a l’étude des systèmes de suspensions semi-actifs. Ce travail est divisé en trois domaines principaux: développement et l’application des techniques LPV pour l’identification des défauts sur les actionneurs dans les systèmes de suspension; développement et mise-en-œuvre d’un système de contrôle prédictif basé sur modèle appliqué en temps réel sur des suspensions semi-actifs; développement des techniques LPV de reconfiguration pour la commande tolerant aux défauts des systèmes de suspension. Les stratégies de commande développées sont analysées par simulation et validation et se montrent satisfaisantes.This End-of-Studies Work presents a range of techniques of Modeling, Identification and Advanced Control applied to the study of Semi-Active Suspensions in Vehicular Systems. This work is divided into three main branches: i) development and application of LPV fault identification techniques on actuators of suspension systems; ii) development and implementation of a real-time model predictive control scheme applied the control of semi- active suspensions; iii) development and application of LPV reconfiguration techniques for fault tolerant control of suspension system. The developed control strategies are analysed through simulation and validation on a mechatronic test-bench and prove themselves satisfactory.Este Trabalho de Conclusão de Curso apresenta diversas técnicas de Modelagem, Identifi- cação e Controle Avançado aplicadas ao estudo de Suspensões Semi-Ativas em Sistemas Veiculares. Este trabalho é divido em três eixos principais: i) Desenvolvimento e aplicação de técnicas LPV de Identificação de Falhas em amortecedores de sistemas de suspensão; ii) Desenvolvimento e implementação de um sistema de Controle Preditivo baseado em modelo aplicado em tempo-real para o controle de suspensões semi-ativas; iii) Desenvolvimento e aplicação de técnicas de reconfiguração LPV para o Controle Tolerante a Falhas de sistemas de suspensão. As técnicas e o desenvolvimento feito são analisados através de simulação e validação em uma plataforma mecatrônica experimental e demonstram-se satisfatórios

Teaching control with Basic Maths: Introduction to Process Control course as a novel educational approach for undergraduate engineering programs

In this article, we discuss a novel education approach to control theory in undergraduate engineering programs. In particular, we elaborate on the inclusion of an introductory course on process control during the first years of the program, to appear right after the students undergo basic calculus and physics courses. Our novel teaching proposal comprises debating the basic elements of control theory without requiring any background on advanced mathematical frameworks from the part of the students. The methodology addresses, conceptually, the majority of the steps required for the analysis and design of simple control systems. Herein, we thoroughly detail this educational guideline, as well as tools that can be used in the classroom. Furthermore, we propose a cheap test-bench kit and an open-source numerical simulator that can be used to carry out experiments during the proposed course. Most importantly, we also assess on how the Introduction to process control course has affected the undergraduate program on Control and Automation Engineering at Universidade Federal de Santa Catarina (UFSC, Brazil). Specifically, we debate the outcomes of implementing our education approach at UFSC from 2016 to 2023, considering students' rates of success in other control courses and perspectives on how the chair helped them throughout the course of their program. Based on randomised interviews, we indicate that our educational approach has had good teaching-learning results: students tend to be more motivated for other control-related subjects, while exhibiting higher rates of success.Comment: 55 pages, 13 figures, Screening at the Journal of Control, Automation and Electrical System

Nonlinear Data-Driven Control Part II: qLPV Predictive Control using Parameter Extrapolation

We present a novel data-driven Model Predictive Control (MPC) algorithm for nonlinear systems. The method is based on recent extensions of behavioural theory and Willem's Fundamental Lemma for nonlinear systems by the means of adequate Input-Output (IO) quasi-Linear Parameter Varying (qLPV) embeddings. Thus, the MPC is formulated to ensure regulation and IO constraints satisfaction, based only on measured datasets of sufficient length (and under persistent excitation). Instead of requiring the availability of the scheduling trajectories (as in recent papers), we consider an estimate of the function that maps the qLPV realisation. Specifically, we use an extrapolation procedure in order to generate the future scheduling trajectories, at each sample, which is shown to be convergent and generated bounded errors. Accordingly, we discuss the issues of closed-loop IO stability and recursive feasibility certificates of the method. The algorithm is tested and discussed with the aid of a numerical application.Comment: 21 pages, 4 figure

Advanced Control for Energy Management of Grid-Connected Hybrid Power Systems in the Sugar Cane Industry

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.Ministerio de Economía y Competitividad CNPq401126/2014-5Ministerio de Economía y Competitividad CNPq303702/2011-7Ministerio de Economía y Competitividad DPI2016-78338-

Tutorial: Implementando Controladores Preditivos N\~ao Lineares atrav\'es do Ferramental LPV

Recent works have demonstrated how Linear Parameter Varying Model Predictive Control (LPV MPC) algorithms are able to control nonlinear systems with precision and reduced computational load. Specifically, these schemes achieve comparable performances to state-of-the-art nonlinear MPCs, while requiring the solution of only one quadratic programming problem (thus being real-time capable). In this tutorial paper, we provide a step-by-step overview of how to implement such LPV MPC algorithms, covering from modelling to stability aspects. For illustration purposes, we consider a realistic implementation for a gas-lift petroleoum extraction process, comparing the LPV approach with the becnhmark nonlinear MPC software CasADi.Comment: Main text in portuguese, submitted to the 2023 Simp\'osio Brasileiro de Automa\c{c}\~ao Inteligente (SBAI 2023

Sustainable energy technologies for the Global South: Challenges and solutions toward achieving SDG 7

The United Nations (UN) expectations for 2030 account for a renewable, affordable, and eco-friendly energy future. The 2030 agenda includes 17 different Sustainable Development Goals (SDGs) for countries worldwide. In this work, the 7th SDG: Affordable and Clean Energy, is brought into focus. For this goal, five main challenges are discussed: (i) limiting the use of fossil fuels; (ii) migrating towards diversified and renewable energy matrices; (iii) decentralizing energy generation and distribution; (iv) maximizing energy and energy storage efficiency; and (v) minimizing energy generation costs of chemical processes. These challenges are thoroughly scrutinized and surveyed in the context of recent developments and technologies including energy planning and supervision tools employed in the Global South. The discussion of these challenges in this work shows that the realization of SDG 7, whether partially or in full, within the Global South and global contexts, is possible only if existing technologies are fully implemented with the necessary international and national policies. Among the key solutions identified in addressing the five main challenges of SDG 7 are a global climate agreement; increased use of non-fossil fuel energy sources; Global North assistance and investment; reformed global energy policies; smart grid technologies and real time optimization and automation technologies

Sustainable energy technologies for the Global South: challenges and solutions toward achieving SDG 7

The United Nations (UN) expectations for 2030 account for a renewable, affordable, and eco-friendly energy future. The 2030 agenda includes 17 different Sustainable Development Goals (SDGs) for countries worldwide. In this work, the 7th SDG: Affordable and Clean Energy, is brought into focus. For this goal, five main challenges are discussed: (i) limiting the use of fossil fuels; (ii) migrating towards diversified and renewable energy matrices; (iii) decentralizing energy generation and distribution; (iv) maximizing energy and energy storage efficiency; and (v) minimizing energy generation costs of chemical processes. These challenges are thoroughly scrutinized and surveyed in the context of recent developments and technologies including energy planning and supervision tools employed in the Global South. The discussion of these challenges in this work shows that the realization of SDG 7, whether partially or in full, within the Global South and global contexts, is possible only if existing technologies are fully implemented with the necessary international and national policies. Among the key solutions identified in addressing the five main challenges of SDG 7 are a global climate agreement; increased use of non-fossil fuel energy sources; Global North assistance and investment; reformed global energy policies; smart grid technologies and real time optimization and automation technologies

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