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An Inverse Approach to Intermediate Point Norm Optimal Iterative Learning Control with Auxiliary Optimization

An Inverse Approach to Intermediate Point Norm Optimal Iterative Learning Control with Auxiliary Optimization
An Inverse Approach to Intermediate Point Norm Optimal Iterative Learning Control with Auxiliary Optimization
Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimization-based Iterative Learning Control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimizing a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known Norm Optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.
0020-3270
1646-1671
Owens, D H
d1838c62-b96e-4710-9e5a-ed097fae28f6
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, B
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D H
d1838c62-b96e-4710-9e5a-ed097fae28f6
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, B
555a86a5-0198-4242-8525-3492349d4f0f

Owens, D H, Freeman, C T and Chu, B (2014) An Inverse Approach to Intermediate Point Norm Optimal Iterative Learning Control with Auxiliary Optimization. International Journal of Control, 87 (8), 1646-1671. (doi:10.1080/00207179.2014.880951).

Record type: Article

Abstract

Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimization-based Iterative Learning Control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimizing a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known Norm Optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.

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Published date: 1 June 2014
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 344294
URI: http://eprints.soton.ac.uk/id/eprint/344294
ISSN: 0020-3270
PURE UUID: 4b733298-ab99-4027-9ce7-d2b1f4bb72bb

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Date deposited: 16 Oct 2012 23:18
Last modified: 29 Oct 2019 01:40

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