Please use this identifier to cite or link to this item: http://www.repositorio.ufc.br/handle/riufc/55347
Title in Portuguese: Reference trajectory tracking control of a nonholonomic mobile robotwith inertial sensor fusion
Author: Forte, Marcus Davi do Nascimento
Advisor(s): Nogueira, Fabrício Gonzalez
Co-advisor(s): Correia, Wilkley Bezerra
Keywords: Mobile Robot
Sensor Fusion
LQR Control
Kalman Filter
Issue Date: 2018
Citation: FORTE, Marcus Davi do Nascimento. Reference trajectory tracking control of a nonholonomic mobile robotwith inertial sensor fusion. 2018. 88 f. Dissertação (mestrado) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica, Fortaleza, 2018.
Abstract in Portuguese: This work presents a study on differential drive wheeled mobile robots regarding its localization estimation using sensor fusion techniques and control over a reference trajectory. The robot’s posture is an extremely important variable to be estimated, specially for autonomous mobile robots as it has to drive along a path without manual intervention. Posture estimation provided by individual sensors has shown to be inaccurate or straightforward mismatched, leading to a need of data fusion from different sources. Sensors such as accelerometer, gyroscope and magnetometer each have its own intrinsic constructive limitations. However, by combining all their flaws into a linear model allows optimal estimators, such as Kalman Filter (KF), to be used and produce estimates close to real life behavior. For the trajectory tracking and disturbance rejection, a linear control strategy for a linearized mobile robot model is applied. The system is modeled with error states to be carried out by a Linear Quadratic Regulator (LQR) controller along with a feedforward reference control action so that the reference trajectory is accordingly tracked.
Abstract: This work presents a study on differential drive wheeled mobile robots regarding its localization estimation using sensor fusion techniques and control over a reference trajectory. The robot’s posture is an extremely important variable to be estimated, specially for autonomous mobile robots as it has to drive along a path without manual intervention. Posture estimation provided by individual sensors has shown to be inaccurate or straightforward mismatched, leading to a need of data fusion from different sources. Sensors such as accelerometer, gyroscope and magnetometer each have its own intrinsic constructive limitations. However, by combining all their flaws into a linear model allows optimal estimators, such as Kalman Filter (KF), to be used and produce estimates close to real life behavior. For the trajectory tracking and disturbance rejection, a linear control strategy for a linearized mobile robot model is applied. The system is modeled with error states to be carried out by a Linear Quadratic Regulator (LQR) controller along with a feedforward reference control action so that the reference trajectory is accordingly tracked.
URI: http://www.repositorio.ufc.br/handle/riufc/55347
metadata.dc.type: Dissertação
Appears in Collections:DEEL - Dissertações defendidas na UFC

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