In this thesis, filtering, estimation and indirect adaptive control algorithms applied to bilateral teleoperation systems are proposed. The objective of this work was to identify and to implement indirect adaptive control in a slave master system. In a growing scenario of remote access to environments and equipment, as in industry 4.0, there is a need to quantify and characterize dynamic system parameters in order to improve the performance of control systems.In this manuscript three contributions regarding the teleoperated systems estimation and control are proposed. First, the Extended Kalman Filter (EKF) algorithm was combined with the Recursive Least Squares State-Variable Filter (RLSSVF), giving rise to the Hybrid Algorithm (AH1). The AH1 was able to estimate the parameters of a continuous time variant system in the state space form. In the second contribution, the Kalman Filter (KF) algorithm was combined with the Recursive Least Squares Estimator with Forgetting Factor Ú (RLSEFFÚ), originating the Hybrid Algorithm (AH2). AH2 was able to estimate specific parameters of a continuous time variant state space system. In the last contribution, the Method for Parameter and Delay Time Estimation (MEPTA) was combined with the Pole Allocation Method, giving rise to the Hybrid Algorithm for Identification and Adaptive Control (AHICA). Thus, AHICA was able to perform indirect adaptive control of a process with discrete time variant parameters. Such control system meets transient and stationary constraints, as well as the setpoint tracking.The proposed methods presented a good performance, especially regarding the algorithms coupling, what can be verified through the simulated results. Such methods can be applied to similar problems from new mathematical arrangements and computational constructions.
Editora Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterála de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.
This article performed the design, simulation, and performance analysis between two parametric estimation algorithms, the Gradient Method (MG) and the Recursive Least Squares Method (MMQR), both applied to the Adaptive Control by Reference Model (CAMR) system. The study of design techniques and control analysis, as well as the comparison of the methods presented here, enhance the ability of the designer to deal with practical problems effectively. The main contribution of the article was to apply and clarify the advantages of the methods presented. Thus, the specific objectives were: identify the plant to be controlled; discretize the plant; discretize the plan (iii) build the control law; implement the identification algorithm; and analyze and analyze the simulated results. From numerical simulations, we analyzed the performance of each algorithm and its respective advantages, advantages, and limitations. The MMQR has an excellent transient regime, but its computational cost was high. The MG has the slowest accommodation time and has low computational demand when compared to the MMQR. By taking into account the characteristics of each algorithm and having prior knowledge about the plant you want to control, such previous information helps you choose the algorithm, thus enhancing the better performance of the control system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.