Spatial iterative learning control: systems with input saturation
Spatial iterative learning control: systems with input saturation
This paper proposes a novel Iterative Learning Control (ILC) framework for spatial tracking. Spatial tracking means that the temporal component is not fixed which violates the standing assumption on time intervals in traditional ILC. The proposed framework allows for the length of the time interval to change with each iteration. To relate the spatial information from the past to the present iteration, the concept of spatial projection is proposed. A class of nonlinear uncertain systems with input saturation is chosen for demonstration. An a appropriate ILC control law, exploiting the spatial projection idea, is proposed and the corresponding convergence analysis, based on the Composite Energy Function, is carried out. It is shown that spatial tracking is achieved under appropriate assumptions related to spatial projection and provided that the desired trajectory is realizable within the saturation bound. Finally, simulation results illustrate the predicted convergence.
1-6
Ljesnjanin, Merid
6510674a-d142-43db-bddf-35a488d340f1
Tan, Ying
23bafadb-0655-48fe-9937-c59f01cb58ab
Oetomo, Denny
88a00d0d-e242-411c-9075-536628e0c782
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Ljesnjanin, Merid
6510674a-d142-43db-bddf-35a488d340f1
Tan, Ying
23bafadb-0655-48fe-9937-c59f01cb58ab
Oetomo, Denny
88a00d0d-e242-411c-9075-536628e0c782
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Ljesnjanin, Merid, Tan, Ying, Oetomo, Denny and Freeman, Christopher
(2017)
Spatial iterative learning control: systems with input saturation.
In American Control Conference (ACC), 2017.
IEEE.
.
(doi:10.23919/ACC.2017.7963749).
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper proposes a novel Iterative Learning Control (ILC) framework for spatial tracking. Spatial tracking means that the temporal component is not fixed which violates the standing assumption on time intervals in traditional ILC. The proposed framework allows for the length of the time interval to change with each iteration. To relate the spatial information from the past to the present iteration, the concept of spatial projection is proposed. A class of nonlinear uncertain systems with input saturation is chosen for demonstration. An a appropriate ILC control law, exploiting the spatial projection idea, is proposed and the corresponding convergence analysis, based on the Composite Energy Function, is carried out. It is shown that spatial tracking is achieved under appropriate assumptions related to spatial projection and provided that the desired trajectory is realizable within the saturation bound. Finally, simulation results illustrate the predicted convergence.
Text
Spatial Iterative Learning Control: Systems with Input Saturation
- Accepted Manuscript
More information
Accepted/In Press date: 1 January 2017
e-pub ahead of print date: 3 July 2017
Venue - Dates:
2017 American Control Conference, , Seattle, United States, 2017-05-24 - 2017-05-26
Organisations:
EEE
Identifiers
Local EPrints ID: 401030
URI: http://eprints.soton.ac.uk/id/eprint/401030
ISSN: 2378-5861
PURE UUID: 6e2c9193-31af-4b72-9731-046a9df686d3
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Date deposited: 01 Oct 2016 13:48
Last modified: 15 Mar 2024 15:56
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Contributors
Author:
Merid Ljesnjanin
Author:
Ying Tan
Author:
Denny Oetomo
Author:
Christopher Freeman
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