Systems and control

Processes optimisation

The issue of sustainable development has drawn considerable attention worldwide, and stimulated researchers and engineers to make greater efforts to reduce the cost/benefit-ratio for development and manufacture of bio-industrial processes both economically and environmentally.

We have developed an extended recurrent neural network model for a fed-batch fermentation of Saccharomyces cerevisiae. The aim of building this neural network model is to maximise the quantity of biomass by optimising the feed rate profiles.

Synthesis and analysis of hybrid systems

Hybrid systems typically consist of signals that take values from a continuous set, and also variables that take values from a discrete, typically finite set. Some signals could be time-driven while others could be event-drive in an asynchronous manner. The objective of this research is to conduct a general investigation on synthesis and analysis of hybrid systems using hybrid automata.

Control design of nonlinear dynamical systems with multiple time scales

Dynamical systems with multiple time scales are also called singularly perturbed systems or slow-fast systems. The aim of this research is to conduct a general investigation on:

• Approximating a nonlinear dynamical system with multiple time scales using a fuzzy linear dynamical system with multiple time scales
• Designing a controller for a nonlinear dynamical system with multiple time scales.
  

This proposal is motivated by the need to have singular perturbation analysis tools for nonlinear dynamical systems with multiple time scales

Other research activities

• Kidney modelling
• Nonlinear control
• Modelling and control of water treatment plants
• Bit stream implementation of controllers
• Nonlinear filtering design
• Active fault tolerant control
• System identifications

• Professor Sing Kiong Nguang
• Dr Nitish Patel
• Associate Professor Akshya Swain