This research area focuses on the development, design and application of different methodologies for nonlinear, adaptive and intelligent control systems. Activities in this area include work on control of nonlinear mechatronic and robotic systems, as well as the investigation of dual adaptive control methodologies. These  include an extension of the neural network dual adaptive control schemes designed by the department for trajectory control of mobile robots, to more complex and generic multiple-input/multiple-output nonlinear systems. The performance of various estimation algorithms for real-time adjustment of the neural network weights are also  analysed and evaluated. Plans are currently in progress to integrate the techniques of recent advances in Artificial Intelligence and Machine Learning, such as deep learning and reinforcement learning, with these control system methodologies.

In addition to developing theoretical results and novel control algorithms, practical applications are identified and solutions that  bridge the several theory-practice gaps prevalent in this area are proposed and tested  through implementation on physical, laboratory-scale pilot plant.

This research area focuses on the design, development and implementation of control systems for wheeled mobile robots and robotic manipulators, in order to enhance their capabilities through increased levels of accuracy and autonomy. Control algorithms developed in the Department include those for trajectory tracking of mobile robots in conjunction with obstacle avoidance, autonomous robotic exploration including map-building, path-planning and autonomous navigation in populated environments, target search and tracking using vision, Human Robot Interaction (HRI), as well as novel neural network-based algorithms for stochastic-adaptive dynamic control of mobile robots.

Further research in this area includes the investigation of various Simultaneous-Localization and Mapping (SLAM) algorithms for wheeled mobile robots, and projects on trajectory control of robotic manipulators based on feedback through camera inputs. 

Natural systems, such as weather and climate change dynamics or the invasion patterns of birds, all exhibit complex spatio-temporal behaviour. This research area focuses on developing new models to efficiently represent this complex spatio-temporal behaviour and provide methods for estimation of these models from gathered spatio-temporal data.