The structure and function of the human spinal connectome. This project will use complex network analysis to map the interactions between the brain and body, to understand how the central nervous system controls our movements. The project will provide fundamental insights into mechanisms that coordinate activity in the human motor system, and how the breakdown of coordination may lead to movement disorders. By integrating advanced computational analyses with state-of-the-art recording techniques ....The structure and function of the human spinal connectome. This project will use complex network analysis to map the interactions between the brain and body, to understand how the central nervous system controls our movements. The project will provide fundamental insights into mechanisms that coordinate activity in the human motor system, and how the breakdown of coordination may lead to movement disorders. By integrating advanced computational analyses with state-of-the-art recording techniques, the project will generate new knowledge of the neural basis of human motor coordination. Expected outcomes may support future applications to restore motor function through brain stimulation, prosthetics and robotics design.Read moreRead less
Learning deep resilient behaviour for uncertainty-aware autonomy. This research project aims to propose a novel framework for developing uncertainty-aware autonomous systems using deep learning. There are fundamental gaps in our knowledge of deep uncertainty quantification and its application for risk-aware decision making. Novel algorithms will be proposed to reliably generate deep uncertainty estimates with low computational overhead. These estimates will be then exploited by safety-critical s ....Learning deep resilient behaviour for uncertainty-aware autonomy. This research project aims to propose a novel framework for developing uncertainty-aware autonomous systems using deep learning. There are fundamental gaps in our knowledge of deep uncertainty quantification and its application for risk-aware decision making. Novel algorithms will be proposed to reliably generate deep uncertainty estimates with low computational overhead. These estimates will be then exploited by safety-critical systems such as autonomous robots to identify risky actions and avoid catastrophise. Developed algorithms will be implemented on an autonomous robotic system to make it averse to uncertainties. The outcomes will greatly increase reliable telerobotic applications in mining, manufacturing, defence, and health.Read moreRead less
Distributed Estimation, Control and Optimisation for Networked Systems. This project aims to study large scale networked systems in major infrastructures including power networks, transportation networks, internet of things, and other cyber-physical systems. This project is expected to develop new methodology and algorithms for distributed estimation, control and optimisation of these systems. Distributed solutions are essential because traditional techniques which were designed for small system ....Distributed Estimation, Control and Optimisation for Networked Systems. This project aims to study large scale networked systems in major infrastructures including power networks, transportation networks, internet of things, and other cyber-physical systems. This project is expected to develop new methodology and algorithms for distributed estimation, control and optimisation of these systems. Distributed solutions are essential because traditional techniques which were designed for small systems are not suitable for efficient operations of large scale systems. Application examples include distributed state estimation for power networks, control of multi-agent systems and optimal scheduling of transportation networks. The outcomes of this project are vital to the understanding and management of these systems. Read moreRead less
A New Approach to Sampled-Data Control Design for Nonlinear Systems. This project aims to exploit new sampling and sampled-data modelling insights to bridge the continuous/sampled-data gap in the control of nonlinear systems. The goal is to investigate the impact of these insights on the control design problem and provide a new class of digital control laws for continuous time non-linear systems.
Robust control of power electronics and drives: a synthesis of traditional and model predictive control approaches. This project aims to generate high-performance strategies for the control of power converters. Through the combination of traditional and modern approaches, the project will develop methods which are more reliable and give better energy efficiency than current state of the art techniques.
Robust control of mobile networked systems. The conceptual advances with new design rules are to be developed in the area of robust control of mobile networked systems. A major benefit of the research to be carried out in this project will be its direct application to industrial control problems in the defence, communications and robotics industries and to the management of the environment.
Efficient and high-precision system identification in quantum cybernetics. This project aims to develop new theories and algorithms to enhance system identification capabilities in quantum cybernetics from the perspective of systems and control. The project is anticipated to advance key knowledge and provide effective methods to enable identification of microsystems for wide applications arising in this emerging technology revolution. The intended outcomes are fundamental theories, and efficient ....Efficient and high-precision system identification in quantum cybernetics. This project aims to develop new theories and algorithms to enhance system identification capabilities in quantum cybernetics from the perspective of systems and control. The project is anticipated to advance key knowledge and provide effective methods to enable identification of microsystems for wide applications arising in this emerging technology revolution. The intended outcomes are fundamental theories, and efficient estimation methods for identifying these systems. This project will make important contributions to accelerating practical applications of new technology, and deliver new knowledge and skills for Australia's future industries, which will benefit Australia's economic growth.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101623
Funder
Australian Research Council
Funding Amount
$456,450.00
Summary
High-Fidelity Motion Simulator using Sickness-Free Motion Cueing Algorithm. This project aims to address the key deficiencies of driving and flight simulators by developing novel human perception-based motion cueing algorithms (MCAs) and leveraging advanced artificial intelligence techniques. Despite widespread applications, existing motion simulators fail to deliver the most accurate human sensation to the user. This failure is mainly attributable to the inefficiency and inflexibility of MCAs u ....High-Fidelity Motion Simulator using Sickness-Free Motion Cueing Algorithm. This project aims to address the key deficiencies of driving and flight simulators by developing novel human perception-based motion cueing algorithms (MCAs) and leveraging advanced artificial intelligence techniques. Despite widespread applications, existing motion simulators fail to deliver the most accurate human sensation to the user. This failure is mainly attributable to the inefficiency and inflexibility of MCAs used by simulators. It is expected that this project will significantly increase simulator motion fidelity and eliminate motion sickness. This will have substantial benefits to Australian research communities and industries, particularly where simulators are used for training, performance evaluation and virtual prototyping.Read moreRead less
Exploring new tools in nonlinear filtering and control. The conceptual advances with new design rules to be developed in the area of nonlinear filtering and control. Major benefits of this project will be its direct applications to state estimation and control problems in automobile, manufacturing, military hardware and medical device industries, and its increased capacity of contact research.
Safe, Plug and Play, Multi Agent Dynamic Systems. From driverless cars, to networks of nano satellites, and complex biological networks, the modern world has many examples of multi agent dynamic systems that need careful coordination and control to perform correctly. In many cases, these systems are built up using designs based on intuition, computer simulations and empirical testing. However, there is a clear need to advance the fundamental understandings of such systems: (i) Verifiable overall ....Safe, Plug and Play, Multi Agent Dynamic Systems. From driverless cars, to networks of nano satellites, and complex biological networks, the modern world has many examples of multi agent dynamic systems that need careful coordination and control to perform correctly. In many cases, these systems are built up using designs based on intuition, computer simulations and empirical testing. However, there is a clear need to advance the fundamental understandings of such systems: (i) Verifiable overall dynamic system properties need to be derived to give assurance of performance in situations not previously envisaged; (ii) It is also critical to understand stable system behaviours not just with fixed configurations, but with agile configurations such as splitting, merging, and morphingRead moreRead less