Annie Wang

Annie Wang

Greater Sydney Area
582 followers 500+ connections

Activity

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Experience

  • Bitwise Agronomy Graphic

    Bitwise Agronomy

    Sydney, New South Wales, Australia

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    Sydney, New South Wales, Australia

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    Sydney, Australia

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    Sydney, New South Wales, Australia

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    Sydney, New South Wales, Australia

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    Tokyo, Japan

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    Suzhou, Jiangsu, China

Education

  • UNSW Graphic

    UNSW

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    Some key work has been published here: https://www.sciencedirect.com/science/article/abs/pii/S0168169921001411
    https://www.sciencedirect.com/science/article/abs/pii/S0168169920307006

    Other related videos and material for Automatic Apple Flower Monitoring System:
    http://www.robotics.unsw.edu.au/srv/project/apple-sensing.html

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    Thesis with Associate Professor Jay Katupitiya
    Model Predictive Control for Unmanned Agricultural Ground Vehicle (UAGV) Guidance
    ● Ground vehicle modelling using C++ and Matlab.
    ● Proposed a novel model predictive control method for guidance control of three different kinds of ground vehicles (tractor, tractor-trailer, and tracked vehicle-trailer) in the presence of slip.
    ● Implement the proposed controller in both kinematic simulation and dynamic simulation using C++ and Matlab;…

    Thesis with Associate Professor Jay Katupitiya
    Model Predictive Control for Unmanned Agricultural Ground Vehicle (UAGV) Guidance
    ● Ground vehicle modelling using C++ and Matlab.
    ● Proposed a novel model predictive control method for guidance control of three different kinds of ground vehicles (tractor, tractor-trailer, and tracked vehicle-trailer) in the presence of slip.
    ● Implement the proposed controller in both kinematic simulation and dynamic simulation using C++ and Matlab; by simply changing some parameters, the proposed method is really easy to adapt to control different vehicles.
    ● Test the proposed controller in an autonomous tractor in the field at Elizabeth Macarthur Agricultural Institute, Menangle, NSW.
    ● Generate peer reviewed international publications.

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    Activities and Societies: School Photograph Contest (1st prize); Liaoning Province Mathematical modelling Contest; Performer in End of Year Celebration;

    Thesis: Design of truck tyre changer

Licenses & Certifications

  • UNSW Engineering -Sessional Teaching Staff Development Program 2018 Graphic

    UNSW Engineering -Sessional Teaching Staff Development Program 2018

    UNSW

    Issued
  • National Computer Rank Examination Certificate (The C Language)

    National Education Examination Authority the Ministry of Education of China

Publications

  • Robust model predictive control for path tracking of a tracked vehicle with a steerable trailer in the presence of slip

    5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016 — Seattle, WA, USA

    In comparison to general autonomous robots, high precision guidance of farm vehicles becomes more complex and challenging as these vehicles are inevitably subjected to significant slip caused by very rough and unsteady terrain which is sometimes undulating. Therefore, it is essential to design controllers that have the capability to react to significant and uncertain disturbances. This paper presents a robust model predictive controller for path tracking of a tracked vehicle towing a steerable…

    In comparison to general autonomous robots, high precision guidance of farm vehicles becomes more complex and challenging as these vehicles are inevitably subjected to significant slip caused by very rough and unsteady terrain which is sometimes undulating. Therefore, it is essential to design controllers that have the capability to react to significant and uncertain disturbances. This paper presents a robust model predictive controller for path tracking of a tracked vehicle towing a steerable trailer in the presence of unknown but bounded disturbances. Based on kinematics and a virtual spring, a virtual error vector model with slip is used to model path offset and orientation offset for the tracked vehicle and the trailer. An adaptive min-max model predictive control method is proposed to guarantee robustness and accuracy in the path tracking. Finally, the proposed controller is compared with a min-max model predictive controller and a sliding mode controller in a realistic dynamic simulation platform. The results prove that the proposed adaptive min-max model predictive controller provides the required accuracy and robustness in the presence of slip.

    Other authors
    • Javad Taghia
    • Jay Katupitiya
    See publication
  • A sliding mode controller with a nonlinear disturbance observer for a farm vehicle operating in the presence of wheel slip(Link)

    Autonomous Robots/Springer

    A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the…

    A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the practicality of its application with both matched and unmatched uncertainties considered in the controller design. The controller is designed using an offset model derived from the kinematic model and its operation is verified by simulation and field experiments. In the simulations, the kinematic model based controller is used to control both a kinematic model and a dynamic model of a tractor to verify the performance of the kinematic model based controller. The proposed controller is compared with two other nonlinear controllers, namely, back stepping control and model predictive control. In the field experiments, the three controller were used to control the physical tractor to follow a specified path. Simulation and experimental results are presented to show that the proposed controller demonstrated the required robustness and accuracy at all times.

    Other authors
    • Javad Taghia
    • Stanley Lam
    • Jay Katupitiya
    See publication

Honors & Awards

  • Excellent Graduate Awards

    Northeastern University

  • The City of Shenyang Excellent Undergraduate Student

    The City of Shenyang Educational and Scientific Committee

  • National Scholarship

    Ministry of Education of the People's Republic of China

    The top-level scholarship set up by central government

  • Student Leadership Awards

    Northeastern University

  • STX Named Scholarship

    STX (Dalian) Shipbuilding Co., Ltd.

  • CAI Guan-Shen Named Scholarship

    Sunwah Group

  • Excellent Undergraduate Student Awards

    Northeastern University

  • Excellent Undergraduate Student Scholarship, 1st Class (2009-2010, 3 times)

    Northeastern University

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