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Details of Grant 

EPSRC Reference: GR/R64193/01
Title: Predictive Control of Nonlinear Systems Using Feedback Inearis based on dynamic neural networks.
Principal Investigator: Becerra, Professor VM
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Cybernetics
Organisation: University of Reading
Scheme: Fast Stream
Starts: 01 April 2002 Ends: 31 March 2005 Value (£): 54,075
EPSRC Research Topic Classifications:
Control Engineering
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
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Panel History:  
Summary on Grant Application Form
Predictive control is a technique that uses a model of the controlled system to calculate the control action based on optimal predictions. Predictive control has been very successful due to its ability to accommodate constraints and multivariable systems and also due to the use of empirical dynamic models. Most conventional predictive controllers use linear empirical models. Feedback linearisation is a well known nonlinear control technique that consists of transforming a nonlinear system into a linear system by means of state feedback and nonlinear transformations. Dynamic neural networks are mathematical structures that can be described by means of differential or difference equations and have the ability, given appropriate training, to approximate various types of nonlinear dynamic systems. They are particularly suitable for efficiently modelling dynamic systems with multiple inputs and multiple outputs. The research will integrate predictive control with feedback linearisation based on dynamic neural networks, with particular emphasis on multiple-input multiple-output systems, and focusing on the handling of constraints on the input variables (which become nonlinear and state dependent with the nonlinear transformations used), the structure of the empirical models, the training of the dynamic neural networks, and the effects of the nonlinear transformation used on control performance. Real time experiments will be carried out on a laboratory scale crane system that is able to position a payload in three dimensions and comparisons will be made with conventional control schemes.
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