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Field of Research : Applied Mathematics
Research Topic : control
Field of Research : Optimisation
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  • Funded Activity

    ARC Future Fellowships - Grant ID: FT170100460

    Funder
    Australian Research Council
    Funding Amount
    $780,000.00
    Summary
    Decentralisation and robustness for practical control of complex systems. This project aims to develop the theory and tools to address the control of complex interconnected systems. There is currently an enormous disconnect in decentralised control between the celebrated theoretical advances and the concepts that are used for implementation, or even for computation. The project expects to isolate the key reasons for this disconnect and develop ways to address the control of complex interconnecte .... Decentralisation and robustness for practical control of complex systems. This project aims to develop the theory and tools to address the control of complex interconnected systems. There is currently an enormous disconnect in decentralised control between the celebrated theoretical advances and the concepts that are used for implementation, or even for computation. The project expects to isolate the key reasons for this disconnect and develop ways to address the control of complex interconnected systems. The expected outcome is a tool which can observe information from only a small portion of a network but which may ultimately effect a large portion of the network. This includes smart building management, multi-vehicle systems and convoys, irrigation networks, large array telescopes, and the power distribution grid.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220102759

    Funder
    Australian Research Council
    Funding Amount
    $375,000.00
    Summary
    Optimisation of piezoelectric metamaterials: Towards robotic stress sensors. This project aims to design new piezoelectric material microstructures that can enhance the measurement of complex local stress states within robotic limbs. The project expects to generate new knowledge of the achievable properties of multi-poled piezoelectric materials and develop computational tools for the analysis and structural optimisation of such materials. The designed microstructures may revolutionise piezoelec .... Optimisation of piezoelectric metamaterials: Towards robotic stress sensors. This project aims to design new piezoelectric material microstructures that can enhance the measurement of complex local stress states within robotic limbs. The project expects to generate new knowledge of the achievable properties of multi-poled piezoelectric materials and develop computational tools for the analysis and structural optimisation of such materials. The designed microstructures may revolutionise piezoelectric sensor technology. Expected outcomes include manufactured proof-of-concept sensors that enable measurement of local stress fields. This should provide significant benefits, such as improved future robot capability and reliability, and research training for next-generation Australian computational mathematicians.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP180100745

    Funder
    Australian Research Council
    Funding Amount
    $401,706.00
    Summary
    New mathematics for multi-extremal optimization and diffusion tensor imaging. This project aims to establish numerically certifiable mathematical theory and methods for semi-algebraic optimisation problems. Numerically certifiable optimisation principles and techniques are vital for the practical use of optimisation technologies because they can be readily implemented by common computer models and algorithms. Yet no such methodologies exist for multi-extremal, semi-algebraic optimisation problem .... New mathematics for multi-extremal optimization and diffusion tensor imaging. This project aims to establish numerically certifiable mathematical theory and methods for semi-algebraic optimisation problems. Numerically certifiable optimisation principles and techniques are vital for the practical use of optimisation technologies because they can be readily implemented by common computer models and algorithms. Yet no such methodologies exist for multi-extremal, semi-algebraic optimisation problems which are common in modern science and medicine. The expected outcomes of this project include enhanced optimisation methods for diffusion tensor imaging, an emerging technology in brain sciences.
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