Feedback entropy in dynamical systems. This project aims to use the fundamental concept of entropy to help evaluate the decision-making effort in a variety of feedback control systems in science and engineering. This understanding will help develop smarter technologies and algorithms in areas such as manufacturing, vehicular technology and automated irrigation.
Nonstochastic information flows in networked dynamical systems. Feedback control is a crucial element of manufacturing, vehicular and energy systems, and is needed to guarantee hard performance bounds in safety- and mission-critical environments. When these control systems are implemented over communication networks, the amount of information flowing through them becomes a critical determinant of performance. However, the nonprobabilistic control objectives make standard information theory inapp ....Nonstochastic information flows in networked dynamical systems. Feedback control is a crucial element of manufacturing, vehicular and energy systems, and is needed to guarantee hard performance bounds in safety- and mission-critical environments. When these control systems are implemented over communication networks, the amount of information flowing through them becomes a critical determinant of performance. However, the nonprobabilistic control objectives make standard information theory inapplicable. This project aims to develop a novel, nonstochastic theory of information in order to analyse and design networked dynamical systems that obey worst-case performance limits. This will yield robust, probability-free algorithms for distributed control, filtering and causality inference.Read moreRead less
Efficient computational methods for worst-case analysis and optimal control of nonlinear dynamical systems. Natural and technological systems can exhibit extremely complicated behaviour in worst-case scenarios. This project will develop efficient mathematical and computational tools that will enable this behaviour to be understood and controlled.
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
Functional state observers for large-scale interconnected systems. This project will produce conceptual advances with new design rules to develop robust and efficient functional state observers for interconnected systems. The outcomes will advance the theory of functional observers and improve the operation, efficiency and performance of critical infrastructure such as power grids, water and traffic networks.
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. Read moreRead less
Reliable and efficient algorithms for modelling dynamical systems from data. Mathematical and computational models are increasingly important in diverse areas of science and engineering including aircraft and automotive design, robotics, medical sensing, and biology. However, finding an accurate model remains a difficult task. This project will develop new methods to reliably find highly accurate models from recorded data.