May 6, 2022 Journal article Open Access

Saeed Rafee Nekoo; José Ángel Acosta; Guillermo Heredia; Anibal Ollero

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    <subfield code="c">2022-05-06</subfield>
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    <subfield code="a">This Research is funded by the European Commission H2020 Programme under HYFLIERS project contract 779411, AERIAL-CORE project contract number 871479 and the ARTIC (RTI2018-102224-B-I00) project, funded by the Spanish Agencia Estatal de Investigación.</subfield>
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    <subfield code="a">&lt;p&gt;This work proposes a novel proportional-derivative (PD)-type state-dependent Riccati equation (SDRE) approach with iterative learning control (ILC) augmentation. On the one hand, the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers. On the other hand, the SDRE adds nonlinear and optimality characteristics to the controller, i.e. increasing the stability margins. These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning. The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x) for the control input law u=-R^(-1) (x) B^T (x)K(x)x. The sub-blocks of the overall gain R^(-1) (x) B^T (x)K(x), are not necessarily symmetric positive definite. A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u=-K_SP (x)e-K_SD (x) e ̇. The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems; and presents guaranteed uniform boundedness in finite-time between learning loops. The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation (SDDRE) to manipulate the final time. The SDDRE expresses a differential equation with a final boundary condition, which imposes a constraint on time that could be used for finite-time control. So, the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool. The learning rules benefit from the gradient descent method for both regulation and tracking cases. One of the advantages of this approach is a guaranteed-stability even from the first loop of learning. A mechanical manipulator, as an illustrative example, was simulated for both regulation and tracking problems. Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.&lt;/p&gt;</subfield>
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    <subfield code="u">The GRVC Robotics Lab., Depto de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Seville, Spain</subfield>
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    <subfield code="a">José Ángel Acosta</subfield>
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    <subfield code="u">The GRVC Robotics Lab., Depto de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Seville, Spain</subfield>
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    <subfield code="a">Guillermo Heredia</subfield>
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    <subfield code="u">The GRVC Robotics Lab., Depto de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Seville, Spain</subfield>
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    <subfield code="a">Anibal Ollero</subfield>
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    <subfield code="u">The GRVC Robotics Lab., Depto de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Seville, Spain</subfield>
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    <subfield code="a">Saeed Rafee Nekoo</subfield>
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    <subfield code="a">SDRE; SDDRE; Symmetric; PD-type; Closed-loop; Iterative Learning Control.</subfield>
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    <subfield code="a">10.1109/JAS.2022.105533</subfield>
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    <subfield code="a">A PD-Type State-Dependent Riccati Equation with Iterative Learning Augmentation for Mechanical Systems</subfield>
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