An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.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
Uncertainty quantification using type-2 fuzzy systems. This project will develop new interval type-2 fuzzy logic system-based tools for quantifying uncertainties present in complex systems. The outcome of this project will greatly help all Australian industries and organisations that directly or indirectly use model-based estimation for prediction and forecasting purposes.
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
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