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Research Topic : Shellfish Industry
Field of Research : Systems Theory And Control
Australian State/Territory : VIC
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Systems Theory And Control (3)
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  • Funded Activity

    Linkage Projects - Grant ID: LP0882276

    Funder
    Australian Research Council
    Funding Amount
    $153,762.00
    Summary
    Robust Optimal Asset Liability Management via Stochastic Control Theory. The Australian federal and state governments are strongly exposed to the Australian and international investment markets, either directly or through entities such as the Future Fund, state-owned insurers and superannuation schemes. Additionally, the investment pool represented by individual Australian's superannuation savings managed by non-government organisations is significant. Robust and effective management of these .... Robust Optimal Asset Liability Management via Stochastic Control Theory. The Australian federal and state governments are strongly exposed to the Australian and international investment markets, either directly or through entities such as the Future Fund, state-owned insurers and superannuation schemes. Additionally, the investment pool represented by individual Australian's superannuation savings managed by non-government organisations is significant. Robust and effective management of these assets in order to meet future liabilities of these funds are essential to a stable Australian economy. This research has the potential to be a key component of reliable investment management, helping make Australia an important investment hub.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT0991385

    Funder
    Australian Research Council
    Funding Amount
    $686,400.00
    Summary
    Real time optimisation by extremum seeking control and learning control. Optimal control technology provides the systematic design of systems that exhibit optimal behaviour, such as maximal productivity, best efficiency, minimal cost and best quality. Real time optimisation finds the solution of the optimal control in real time, relaxing requirements on the system knowledge. The proposed research will build on Australia's well-established strength in control and optimisation, and aim to establis .... Real time optimisation by extremum seeking control and learning control. Optimal control technology provides the systematic design of systems that exhibit optimal behaviour, such as maximal productivity, best efficiency, minimal cost and best quality. Real time optimisation finds the solution of the optimal control in real time, relaxing requirements on the system knowledge. The proposed research will build on Australia's well-established strength in control and optimisation, and aim to establish within Australia world-leading expertise in real time optimisation theories and applications. This will have direct benefits to the Australian economy through various engineering applications ranging from vehicle dynamics to emissions reduction to manufacturing process to efficiency improvement of power generation systems.
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    Funded Activity

    Discovery Projects - Grant ID: DP0985388

    Funder
    Australian Research Council
    Funding Amount
    $345,000.00
    Summary
    Extremum seeking control: analysis, design and applications. Optimal control is one of the central pillars in the field of automatic control, but is prevented from use in many engineering applications due to the computational complexity and system knowledge requirements typically associated with the technique. Extremum seeking promises the performance of an optimal approach, but with the benefit of real time implementation and very relaxed requirements on the system knowledge. Through improved u .... Extremum seeking control: analysis, design and applications. Optimal control is one of the central pillars in the field of automatic control, but is prevented from use in many engineering applications due to the computational complexity and system knowledge requirements typically associated with the technique. Extremum seeking promises the performance of an optimal approach, but with the benefit of real time implementation and very relaxed requirements on the system knowledge. Through improved understanding of extremum seeking algorithms, applications from vehicle dynamics to emissions reduction to manufacturing processes will benefit with greater levels of performance and robustness.
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