Control at What Cost? One-Shot Real-Time Dual Inverse Optimal Control

Australia is on the cusp of multiple technology transitions, including the renewable energy transition and the widespread adoption of automation across agriculture, manufacturing, mining, transport and logistics, and aerospace. This project will develop the techniques and algorithms required to enable engineered systems to make optimal decisions in uncertain dynamic economic and physical environments by actively imputing (learning or inferring) and optimising uncertain costs and constraints associated with their decisions and actions. The techniques and algorithms developed in this project will benefit Australia and Australians by unlocking more efficient generation and utilisation of electricity in the national energy market through ensuring that generation is more closely matched to consumption; they will improve levels of efficiency and productivity in agriculture, mining, transport and logistics, and manufacturing through the availability of robots and autonomous systems