Development of Intelligent Control Systems Technology for Systems with Fast Dynamics

We seek to develop a technology that can perform online identification and control of any dynamic systems without developing models for the systems.
In the area of flight control, many modern control strategies have been employed for high control performance over a broad operating envelope. Gain-scheduled controllers are traditionally designed on the basis of the dynamics relative to a family of trim conditions assuming that the airspeed is slowly varying. However, during aggressive maneuvering, the vehicle may be far from equilibrium with rapidly varying airspeed. In addition, the requirements to operate at high angles of attack necessitate scheduling on rapidly varying quantities such as the instantaneous incidence angle rather than, for example, conventional flap scheduling on averaged incidence. It should be highlighted that scheduling on instantaneous incidence is well known to lead to instability and is invariably avoided in classical scheduling arrangements. Also, it may be difficult to determine the operating regimes, and how they should be characterised, which is the key to successful modelling and control using these methods.

In recent years, much research effort has been directed towards design of intelligent controllers to handle structure and/or unstructured uncertainties for systems with fast dynamics using fuzzy logic and neural networks. Unfortunately, robustness and reliability have not been fully addressed. Moreover, to our knowledge, the controller is rather complicated and is not suitable for real-time applications.

Our proposed technology will allow one to design and develop a robust adaptive intelligent controller that can cope with a real- life scenario where the environments and the dynamics of a system are time varying. The developed technology can also be employed to cancel noise adaptively so as to facilitate accurate detection of feedback signals in surveillance and sensing system.

Principal Investigator: Assoc. Professor Er Meng Joo

Team Members:
Dr. Tang Zhe, Research Fellow
Dr. Li Mingbin, Research Associate