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Norm optimal iterative learning control for general spatial path following problem

Norm optimal iterative learning control for general spatial path following problem
Norm optimal iterative learning control for general spatial path following problem

This paper takes the advantage of iterative learning control (ILC) to solve spatial path following problems with high tracking accuracy. The concept of spatial path tracking is first introduced by defining a varying reference profile, which specifies the speed along the path. The task description of ILC is extended to incorporate the scope of spatial tracking by enabling its reference profile as a changing variable. Hence, a spatial ILC algorithm with monotonic convergence properties is derived, which updates the input signal and reference profile simultaneously by solving a minimum norm problem. This optimization problem is further reformulated into a second order cone programming (SOCP) problem by linear approximation, and the global optimal solution can be obtained. Based on a gantry robot model, numerical tests are undertaken to demonstrate the feasibility of the proposed algorithm.

2576-2370
4933-4938
IEEE
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chen, Yiyang, Chu, Bing and Freeman, Christopher T. (2019) Norm optimal iterative learning control for general spatial path following problem. In 2018 IEEE Conference on Decision and Control (CDC). vol. 2018-December, IEEE. pp. 4933-4938 . (doi:10.1109/CDC.2018.8619378).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper takes the advantage of iterative learning control (ILC) to solve spatial path following problems with high tracking accuracy. The concept of spatial path tracking is first introduced by defining a varying reference profile, which specifies the speed along the path. The task description of ILC is extended to incorporate the scope of spatial tracking by enabling its reference profile as a changing variable. Hence, a spatial ILC algorithm with monotonic convergence properties is derived, which updates the input signal and reference profile simultaneously by solving a minimum norm problem. This optimization problem is further reformulated into a second order cone programming (SOCP) problem by linear approximation, and the global optimal solution can be obtained. Based on a gantry robot model, numerical tests are undertaken to demonstrate the feasibility of the proposed algorithm.

Full text not available from this repository.

More information

Published date: 21 January 2019
Venue - Dates: 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 2018-12-17 - 2018-12-19

Identifiers

Local EPrints ID: 429135
URI: https://eprints.soton.ac.uk/id/eprint/429135
ISSN: 2576-2370
PURE UUID: 5656ed4c-7d9a-4a13-af97-a65e791c81c3
ORCID for Yiyang Chen: ORCID iD orcid.org/0000-0001-9960-9040
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 22 Mar 2019 17:30
Last modified: 26 Mar 2019 01:23

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