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Inventi Impact: Control Science & Engineering

Articles

  • Inventi:ecse/28908/19
    Explicit GPC Control Applied to an Approximated Linearized Crane System
    Research 2019 : July - September
    Daniel Guerra Vale da Fonseca, André Felipe O de A Dantas, Carlos Eduardo Trabuco Dórea, André Laurindo Maitelli

    This paper proposes aMIMOExplicitGeneralized Predictive Control (EGPC) forminimizing payload oscillation of aGantry Crane\nSystem subject to input and output constraints. In order to control the crane system efficiently, the traditional GPC formulation,\nbased on online Quadratic Programming (QP), is rewritten as a multiparametric quadratic programming problem (mp-QP). An\nexplicit Piecewise Affine (PWA) control law is obtained and holds the same performance as online QP. To test effectiveness, the\nproposed method is compared with two GPC formulations: one that handle constraints (CGPC) and another that does not handle\nconstraints(UGPC).Results showthat both EGPCandCGPChave better performance, reducing the payload swingwhen compared\nto UGPC. Also both EGPC and CGPC are able to control the system without constraint violation. When comparing EGPC to\nCGPC, the first is able to calculate (during time step) the control action faster than the second. The simulations prove that the\noverall performance of EGPC is superior to the other used formulations.

    How to Cite this Article
    Attribution/ CC Compliant Citation: Fonseca, Daniel Guerra Vale\nda, et al. \"Explicit GPC Control Applied to an Approximated\nLinearized Crane System.\" Journal of Control Science and\nEngineering 2019 (2019).\nhttps://doi.org/10.1155/2019/3612634\nhttp://creativecommons.org/licenses/by/4.0/\nSome formatting elements, header, footer, logos, dates and\npagination were modified while adapting this article.
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