The project involves the application of modelling, control and optimization to devise methods for improving the operation of water distribution networks through automation. The project has enjoyed strong support from the industry partner and the team has received a number of awards (e.g. the IEEE CSS Control Systems Technology Award in 2014) for outcomes of the work. Installations of technology arising from the project is operational in Australia, New Zealand, US, China. Challenges range from data-driven modelling of hydraulic networks for control, decentralized and distributed feedback control of networked systems, hierarchical control, constrained and receding horizon control, scalable computation for optimal control and scheduling (demand management)
Researchers involved: Michael Cantoni, Erik Weyer, Peter Dower, Iman Shames, Farhad Farokhi, Iven Mareels
Partners: Rubicon Water, Australian Research Council
In this project, a number of decoys are deployed in defence of a ship or fleet of ships against advanced anti-ship missiles. The autonomous networked platforms (e.g., UAVs or USVs) must optimize their positions/flight trajectories and deliver the right effects to ensure successful defence of the ship(s). It is assumed that the UAVs/USVs have multiple payload functionalities – electronic attack and support, surveillance, and communication relays. The cooperative guidance of the autonomous vehicles will be managed centrally on the command ship. However, it is expected that the autonomous vehicles will be able to perform distributed decision making for improved system robustness and computational burden sharing.
Researchers involved: Iman Shames
The performance of diesel engines in the automotive and maritime industries are intimately linked to the control algorithms used to determine the actuation levels and inputs.Legislative and increasing consumer requirements demand better control approaches than have been deployed in production vehicles to date, and motivate the use of model based techniques to meet performance and emissions specifications. However, the benefits of using advanced controllers are typically tempered by the need to spend more time and money on calibration of the algorithms as the tuning parameters are not related to time domain specifications.
With industry keen to adopt and integrate higher performing algorithms into their platforms, we have been working with researchers at Australia’s Defence Science and Technology Group (DSTG) and Japan’s Toyota Motor Corporation to develop robust yet practical optimisation-based engine controllers that are more easily calibrated.
These projects aim to develop robust multivariable model predictive control algorithms for high performing diesel engines that reduce calibration effort. The algorithms will be implemented at state-of-the-art facilities both within Australia and internationally. The anticipated outcome is new model based control architectures that improve diesel engine operation yet reduce its calibration effort and cost.
Researchers involved: Chris Manzie, Iman Shames, Dragan Nesic, Rohan Shekhar, Gokul Sankar, Noam Olshina
Partners: Toyota Motor Corporation (TMC), Defence Science and Technology Group, Australian Research Council
This project aims to develop models, design and analysis techniques for secure Networked Control Systems (NCS). These could control large-scale and complex distributed systems. Improved NCS technology will underpin our ability to optimise water and energy use, live in sustainable communities and create greater efficiencies in manufacturing and transport globally. Only secure NCS design methodologies can use this emerging technology to deliver benefits while protecting it against cyber-attacks. Modelling and designing secure NCS with specific networks is expected to realise the full potential of existing and emerging technology.
Researchers involved: Dragan Nesic, Iman Shames
Partner: Australian Research Council
In this first instance, a feasible problem formulation is to be investigated (e.g. as an optimisation problem), with some investigation into choosing an appropriate performance measures along with a priori known measurement models. For example, the Fisher information, a measure of adversary detectability. The agent (sensor) control problem will be cast in the multi-armed bandit setting, with actions corresponding to allocating limited resources as well as affecting the sensors (such as moving positions, changing focus, polling, or replacement). Since acquiring information may be limited not only to the number of the heterogeneous sensors but also the cost of different types of sensors in the environment (particularly those based on human sources), the model will incorporate resource supply limitations. These endow actions with budgets, making the optimisation of resources difficult. This problem will be addressed by estimating payoffs and resource consumption based on various rational decision-making models (e.g. minmax, maxmin, etc.), using confidence bounds for both payoffs and resource consumption.
Researchers involved: Iman Shames, Farhad Farokhi, Ben Rubinstein