Details of Grant 

EPSRC Reference:	GR/R94442/01
Title:	Control of Nonlinear Systems Identified using Scheduled, Adaptive and State Dependent Parameter Models
Principal Investigator:	Taylor, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Alstom Group Catholic University Leuven Lancaster University
Department:	Engineering
Organisation:	Lancaster University
Scheme:	Fast Stream
Starts:	01 October 2002	Ends:	30 September 2005	Value (£):	60,259
EPSRC Research Topic Classifications:	Control Engineering
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Summary on Grant Application Form
This fast stream proposal outlines an investigation into two highly nonlinear engineering problems, namely the regulation of micro-climate in an environmental test chamber and tool head positioning for a robotic arm. Both applications have already been built and instrumented at Lancaster University. The former will involve the collaboration of the PI and a PhD student with the Laboratory for Agricultural Buildings Research (Leuven), while the latter builds on existing expertise at Lancaster into robotics. Indeed, the prototype robotic arm considered in the proposal was developed by Construction Robotics Limited and is the world's first tool deployment robot designed for use in construction. The proposed research, which follows from earlier work into Proportional-Integral-Plus (PIP) control system design, considers a range of approaches for controlling such nonlinear systems, including: linearisation about an operating point; gain scheduling; velocity-based linearisation; adaptive control; exact linearisation by feedback; and constraint handling. In particular, the objective is to employ the generic PIP methodology in each case, so as to complete a comparative evaluation of these approaches. Finally, the PI will collaborate with Professor Peter Young to develop a novel approach for the control of a widely applicable class of nonlinear systems, based on the latter's recently proposed state-dependent parameter modelling methodology. Here, the nonlinear system is modelled using a quasi-linear structure in which the parameters vary as functions of the state variables.
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Organisation Website:	http://www.lancs.ac.uk