Industrial Transformation Training Centres - Grant ID: IC190100017
Funder
Australian Research Council
Funding Amount
$3,703,664.00
Summary
ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource ....ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource attributes, maximising value ‘on the fly’. Benefits will include increasing certainty on product quality and maximising throughput and recovery. Outcomes will include new tools to rapidly model geological and geometallurgical uncertainty with sensor inputs, to track the resource to product and enhance interpretation.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
Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL110100281
Funder
Australian Research Council
Funding Amount
$2,777,066.00
Summary
Large-scale statistical machine learning. This research program aims to develop the science behind statistical decision problems as varied as web retrieval, genomic data analysis and financial portfolio optimisation. Advances will have a very significant practical impact in the many areas of science and technology that need to make sense of large, complex data streams.
Real-time global optimisation for distributed parameter control systems. This project aims to develop real-time optimal control algorithms for distributed parameter systems involving both time and spatial variables and multiple time-delays, with a focus on mining and energy applications. Current optimal control algorithms for such systems are too slow for real-time use and often get trapped at local optima, which can be vastly inferior to the global solution. This project will result in a new op ....Real-time global optimisation for distributed parameter control systems. This project aims to develop real-time optimal control algorithms for distributed parameter systems involving both time and spatial variables and multiple time-delays, with a focus on mining and energy applications. Current optimal control algorithms for such systems are too slow for real-time use and often get trapped at local optima, which can be vastly inferior to the global solution. This project will result in a new optimal control framework, underpinned by recent advances in constraint propagation, switching surface optimisation, and input regularisation. It will result in cutting-edge mathematical tools to complement and exploit new technologies and optimise key processes in natural gas liquefaction and zinc and alumina production, increasing efficiency and reducing the ecological footprint. This project will lead to new cutting-edge control algorithms for replacing the inefficient manual operations endemic in Australia’s natural gas and mineral processing plants.Read moreRead less
What learning is there in learning control? This project seeks to establish a meaningful definition and quantifiable measure of learning in the context of adaptive or learning control. The project is designed within the context of human motor skill learning, and assesses the speed of learning and the quality of learning (reflected by the accuracy of the motor task execution). The project plans to use measures to provide a mathematically precise meaning for the notion of learning. The outcome has ....What learning is there in learning control? This project seeks to establish a meaningful definition and quantifiable measure of learning in the context of adaptive or learning control. The project is designed within the context of human motor skill learning, and assesses the speed of learning and the quality of learning (reflected by the accuracy of the motor task execution). The project plans to use measures to provide a mathematically precise meaning for the notion of learning. The outcome has the potential to be applied to the design of technology-assisted training of motor skills, from the recovery of lost motor skills after trauma to the development of elite athletes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101128
Funder
Australian Research Council
Funding Amount
$399,235.00
Summary
Control and filtering of distributed systems with communication-scheduling. This project aims to develop an analysis and design framework to enhance the reliability of the next-generation advanced manufacturing systems with security vulnerability and communication scheduling. Reliable control and filtering of distributed systems is an emerging area of automation and control engineering in the tide of the 4th industrial revolution. Expected outcomes of this project include obtaining analysis crit ....Control and filtering of distributed systems with communication-scheduling. This project aims to develop an analysis and design framework to enhance the reliability of the next-generation advanced manufacturing systems with security vulnerability and communication scheduling. Reliable control and filtering of distributed systems is an emerging area of automation and control engineering in the tide of the 4th industrial revolution. Expected outcomes of this project include obtaining analysis criteria uncovering the effect from communication scheduling and cyber-attacks, and developing a novel framework based on co-design perspective to realize the distributed system design, while being applied in the cooperative control of various robots or manipulators in unmanned factories.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE200100175
Funder
Australian Research Council
Funding Amount
$475,000.00
Summary
A high-payload, high-fidelity haptically-enabled motion simulation facility. An Australian-first motion simulation facility consisting of a high-payload, high-fidelity Stewart platform mounted on a dual-axis linear track is proposed. The facility will allow high acceleration and high vibration manoeuvres, and large displacements through an eight-degrees-of-freedom range of motion. It can carry the entire control compartment of a heavy vehicle, a truck, an ambulance, a train, or a multi-operator ....A high-payload, high-fidelity haptically-enabled motion simulation facility. An Australian-first motion simulation facility consisting of a high-payload, high-fidelity Stewart platform mounted on a dual-axis linear track is proposed. The facility will allow high acceleration and high vibration manoeuvres, and large displacements through an eight-degrees-of-freedom range of motion. It can carry the entire control compartment of a heavy vehicle, a truck, an ambulance, a train, or a multi-operator cockpit of a mining vehicle for simulation. The outcome will provide significant benefits for virtual vehicle prototyping and testing, driver training and behaviour modelling, motion perception and motion sickness research; therefore advancing Australia as the global leader in motion simulation and vehicular technologies.Read moreRead less
Robust Coherent Control Engineering for Quantum Systems and Networks. This project aims to develop new methods for the design of robust coherent controllers for emerging applications to quantum systems and networks. Using robust controllers which are themselves quantum systems, tools from the theory of optimal risk sensitive control aim to enable technological systems to be designed with high levels of performance in the face of unavoidable uncertainties due to imperfect fabrication and interact ....Robust Coherent Control Engineering for Quantum Systems and Networks. This project aims to develop new methods for the design of robust coherent controllers for emerging applications to quantum systems and networks. Using robust controllers which are themselves quantum systems, tools from the theory of optimal risk sensitive control aim to enable technological systems to be designed with high levels of performance in the face of unavoidable uncertainties due to imperfect fabrication and interactions with the environment. The research aims to yield systematic control engineering methods to combat the effects of quantum decoherence which is critical in order to make quantum technologies such as quantum computing truly practical. Applications include computing, secure communications, sensing and simulationsRead moreRead less
Coherent control engineering for state estimation in quantum linear systems. This project aims to develop new methodologies for designing coherent controllers to facilitate optimal estimation in systems incorporating quantum sensors such as optomechanical and atom-interference sensors. New quantum sensors are being developed which have the potential to achieve sensitivities approaching fundamental physical limits. However to fully exploit these devices, this project will develop new control engi ....Coherent control engineering for state estimation in quantum linear systems. This project aims to develop new methodologies for designing coherent controllers to facilitate optimal estimation in systems incorporating quantum sensors such as optomechanical and atom-interference sensors. New quantum sensors are being developed which have the potential to achieve sensitivities approaching fundamental physical limits. However to fully exploit these devices, this project will develop new control engineering and signal processing methods taking into account the fundamental properties of quantum systems and noise. This will enable quantum sensors to be applied to a wide range of applications including transport, medical imaging, civil engineering, and the detection of hazards.Read moreRead less