EPSRC Reference: |
GR/L25554/01 |
Title: |
NON-LINEAR CONTROL USING RECURRENT NEURAL NETWORKS |
Principal Investigator: |
Kambhampati, Dr C |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Cybernetics |
Organisation: |
University of Reading |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
21 October 1996 |
Ends: |
20 October 1999 |
Value (£): |
130,548
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EPSRC Research Topic Classifications: |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
The aim of this proposal is to further the results of the preliminary work (GR/K08000), focusing on the use of recurrent networks in the control of nonlinear systems. The appropriateness of current dynamic network structures will first be studied. This will then lead on to their use in two currently popular controller synthesis techniques: nonlinear Internal Model Control and Globally Linearising Control. The former makes explicit use of the model inverse while the latter uses the model to derive linearising transforms. Particular emphasis will be paid to stability issues in both these approaches. The relative merits of these schemes, in terms of both performance and implementation aspects, will be investigated via a comparative study.The results of this project will provide enhanced insights into the mechanism of recurrent networks and the development of a formal framework for analysing closed loop stability when they are used as the basis for nonlinear controller designs. It is hoped that the results will enable exploitation of neural network technology to its full potential.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.rdg.ac.uk |