Discovery Early Career Researcher Award - Grant ID: DE140101375
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
The forest and the trees: How global brain rhythms facilitate local information processing. One of the greatest challenges in understanding the brain is the enormous range of scales it operates on, from single neurons a few microns across to entire hemispheres on the scale of tens of centimetres. This project will investigate how large-scale brain rhythms influence and facilitate information processing, particularly motor control, among small networks of individual neurons. The research question ....The forest and the trees: How global brain rhythms facilitate local information processing. One of the greatest challenges in understanding the brain is the enormous range of scales it operates on, from single neurons a few microns across to entire hemispheres on the scale of tens of centimetres. This project will investigate how large-scale brain rhythms influence and facilitate information processing, particularly motor control, among small networks of individual neurons. The research questions will be addressed by combining detailed computer simulations with data-driven analyses of empirical human and monkey brain dynamics. The outcomes of this project will provide a richer understanding of how our brains encode and process information, leading to practical benefits such as improved control of artificial limbs.Read moreRead less
A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, inte ....A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, integrated with machine learning techniques, this project expects to develop a novel framework for data-driven control using big process data. The outcomes are expected to benefit the Australian process industry, where many processes are controlled by inadequate logic controllers, by improving their operational efficiency.Read moreRead less
Distributionally robust dynamic optimisation for nonlinear switched system. Biochemical production utilising fermentation processes evidences poor product repeatability. This project aims to control and optimise 1,3-propanediol production via microbial fermentation. 1,3-propanediol is an essential ingredient for many polymeric materials and is present in cosmetics, personal care and cleaning products. New theory and parallel algorithms will be developed for the control and optimisation of the mi ....Distributionally robust dynamic optimisation for nonlinear switched system. Biochemical production utilising fermentation processes evidences poor product repeatability. This project aims to control and optimise 1,3-propanediol production via microbial fermentation. 1,3-propanediol is an essential ingredient for many polymeric materials and is present in cosmetics, personal care and cleaning products. New theory and parallel algorithms will be developed for the control and optimisation of the microbial fermentation of 1,3-propanediol production, where the bacteria kinetic parameters are uncertain without full knowledge of the probability distribution. This theory will also be applicable to other fermentation processes. The project outcomes are expected to significantly improve the productivity of the biochemical engineering industry involving fermentation processes.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
Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achiev ....Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achieve desired dynamic features. This project aims to develop such a data-based approach by controlling latent variable dynamics, using the behavioural systems framework integrated with big data analytics and artificial neural networks. The outcomes are expected to help build a cornerstone for future smart manufacturing.Read moreRead less
Distributed nonlinear control based on differential dissipativity. This project aims to investigate the process control methodologies crucial to smart manufacturing It aims to develop a distributed optimisation-based nonlinear control approach for plant-wide flexible manufacturing, which can achieve time-varying operational targets including production rates and product specifications to meet dynamic market demands. This includes a contraction-based nonlinear distributed control framework that e ....Distributed nonlinear control based on differential dissipativity. This project aims to investigate the process control methodologies crucial to smart manufacturing It aims to develop a distributed optimisation-based nonlinear control approach for plant-wide flexible manufacturing, which can achieve time-varying operational targets including production rates and product specifications to meet dynamic market demands. This includes a contraction-based nonlinear distributed control framework that ensures plant-wide stability at any feasible set-points or references and a distributed economic model predictive control approach that coordinates autonomous controllers to achieve plant-wide economic objectives in a self-organising manner. The outcomes of this project are expected to form a process control framework for next-generation smart plants.Read moreRead less
A networked robotic telescope array for coincident detection of transient phenomena in the optical, gravitational wave, neutrino and radio spectra. An international collaboration of scientists will employ a global network of rapid response robotic telescopes and detectors to study exotic transient phenomena in the early Universe. Potential spin-offs include the application of novel image analysis techniques for identifying and tracking dangerous space junk.
Dissipativity based distributed model predictive control for complex industrial processes. This project will extend and improve the model predictive control technology, which is the most widely used advanced control approach in process industries. The results will potentially benefit the Australian mineral processing industry where many processes are geographically distributed, leading to more cost-effective operation.
Integrated Approach to Plantwide Fault Diagnosis and Fault-tolerant Control. This project aims to develop a new approach to detect and reduce the impact of faults in industrial plants. Operations of modern industrial processes increasingly depend on automatic control systems, which can make the plants susceptible to faults such as sensor/actuator failures. Based on the concept of dissipative systems, the project aims to develop a novel integrated approach to distributed fault diagnosis and fault ....Integrated Approach to Plantwide Fault Diagnosis and Fault-tolerant Control. This project aims to develop a new approach to detect and reduce the impact of faults in industrial plants. Operations of modern industrial processes increasingly depend on automatic control systems, which can make the plants susceptible to faults such as sensor/actuator failures. Based on the concept of dissipative systems, the project aims to develop a novel integrated approach to distributed fault diagnosis and fault-tolerant control for plant-wide processes. It aims to capture the key dynamic features of normal and abnormal processes by their dissipativity properties, and to use these to develop an efficient online fault diagnosis approach based on process input and output trajectories.Read moreRead less