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Mañoso Hierro, María Carolina

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0000-0001-9790-8557
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Mañoso Hierro
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María Carolina
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2015 - 201912020 - 20221
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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Low speed hybrid generalized predictive control of a gasoline-propelled car
    (Elsevier, 2015-07) Madrid, Ángel P. de; Milanés, V.; Romero Hortelano, Miguel; Mañoso Hierro, María Carolina
    Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem. This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.
  • Miniatura
    Publicación
    Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling
    (IEEE, 2022-12-08) Romero Hortelano, Miguel; Mañoso Hierro, María Carolina
    The control strategies based on the methodology known as Model–based Predictive Control (MPC) have been developed and widely adopted to control real plants. This is mainly due to their intrinsic ability to handle constrains and their capacity to predict and optimize the future behavior of the process using a dynamical model of the plant. On the other hand, the mathematical tool known as fractional calculus has been currently used for reformulating the predictive control strategies to reach a better performance adding new control parameters. This work extends the use of fractional operators for the constraints in one type of fractional predictive control strategy known as Fractional–order Generalized Predictive Control (FGPC), interpreting and discussing the results. In addition, a new method to soften constraints using fractional operator is proposed and illustrated with examples, even to adjust the final response of the system. A practical tuning of the rest of controller parameters with the help of a well–known mathematical software is also included to make use of the beneficial characteristics of this fractional predictive formulation.