Non-causal linear optimal control with adaptive sliding mode observer for multi-body wave energy converters
Non-causal linear optimal control with adaptive sliding mode observer for multi-body wave energy converters
As a non-causal optimal control problem, the performance of wave energy converter (WEC) control relies on the accuracy of the future incoming wave prediction. However, the inevitable prediction errors can degrade WEC performance dramatically especially when a long prediction horizon is needed by a WEC non-causal optimal controller. This paper proposes a novel non-causal linear optimal control with adaptive sliding mode observer (NLOC+ASMO) scheme, which can effectively mitigate the control performance degradation caused by wave prediction errors. This advantage is achieved by embedding the following enabling techniques into the scheme: (i) an adaptive sliding mode observer (ASMO) to estimate current excitation force in real-time with explicitly formulated boundary of estimation error, (ii) an auto-regressive (AR) model to predict the incoming excitation force with explicitly formulated boundary of prediction error using a set of latest historical data of ASMO estimations from (i), and (iii) a compensator to compensate for both the estimation error and the prediction error of excitation force. Moreover, the proposed NLOC+ASMO scheme does not cause heavy computational load enabling its real-time implementation on standard computational hardware, which is especially critical for the control of WECs with complicated dynamics. The proposed NLOC+ASMO framework is generic and can be applied to a wide range of WECs, and in this paper we demonstrate the efficacy by using a multi-float and multi-motion WEC called M4 as a case study, whose control problem is more challenging than the widely studied point absorbers. Simulation results show the effectiveness of the proposed control scheme in a wide range of sea states, and it is also found that the controller is not sensitive to change of ASMO parameters.
Excitation force estimation and prediction, M4, Non-causal linear optimal control (NLOC), Sliding mode compensator, Wave energy converter (WEC)
568-577
Zhang, Yao
a4f30318-ab42-4b38-a60d-f7199ff3a02a
Stansby, Peter
18f0cc40-247f-4acb-b0c3-389d92cf71b3
Li, Guang
76def2e4-4cf4-43b3-8b4c-78c7111d8ef3
January 2021
Zhang, Yao
a4f30318-ab42-4b38-a60d-f7199ff3a02a
Stansby, Peter
18f0cc40-247f-4acb-b0c3-389d92cf71b3
Li, Guang
76def2e4-4cf4-43b3-8b4c-78c7111d8ef3
Zhang, Yao, Stansby, Peter and Li, Guang
(2021)
Non-causal linear optimal control with adaptive sliding mode observer for multi-body wave energy converters.
IEEE Transactions on Sustainable Energy, 12 (1), .
(doi:10.1109/TSTE.2020.3012412).
Abstract
As a non-causal optimal control problem, the performance of wave energy converter (WEC) control relies on the accuracy of the future incoming wave prediction. However, the inevitable prediction errors can degrade WEC performance dramatically especially when a long prediction horizon is needed by a WEC non-causal optimal controller. This paper proposes a novel non-causal linear optimal control with adaptive sliding mode observer (NLOC+ASMO) scheme, which can effectively mitigate the control performance degradation caused by wave prediction errors. This advantage is achieved by embedding the following enabling techniques into the scheme: (i) an adaptive sliding mode observer (ASMO) to estimate current excitation force in real-time with explicitly formulated boundary of estimation error, (ii) an auto-regressive (AR) model to predict the incoming excitation force with explicitly formulated boundary of prediction error using a set of latest historical data of ASMO estimations from (i), and (iii) a compensator to compensate for both the estimation error and the prediction error of excitation force. Moreover, the proposed NLOC+ASMO scheme does not cause heavy computational load enabling its real-time implementation on standard computational hardware, which is especially critical for the control of WECs with complicated dynamics. The proposed NLOC+ASMO framework is generic and can be applied to a wide range of WECs, and in this paper we demonstrate the efficacy by using a multi-float and multi-motion WEC called M4 as a case study, whose control problem is more challenging than the widely studied point absorbers. Simulation results show the effectiveness of the proposed control scheme in a wide range of sea states, and it is also found that the controller is not sensitive to change of ASMO parameters.
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e-pub ahead of print date: 28 July 2020
Published date: January 2021
Additional Information:
Funding Information:
Manuscript received March 16, 2020; revised June 16, 2020; accepted June 25, 2020. Date of publication July 28, 2020; date of current version December 16, 2020. This work was supported in part by a research contract from Wave Energy Scotland Control Systems programme, and in part by EPSRC grant’ Launch and Recovery in Enhanced Sea States’ (EP/P023002/1). This work was completed when Y. Zhang was with School of Engineering and Materials Science, Queen Mary University of London. Paper no. TSTE-00277-2020. (Corresponding author: Yao Zhang.) Yao Zhang is with the School of Mechanical and Construction Engineering, Northumbria University, Newcastle NE1 8ST, U.K., and also with the School of Materials Science, Queen Mary University of London, E1 4NS (e-mail: yaozhanghit@outlook.com).
Funding Information:
This work was supported in part by a research contract from Wave Energy Scotland Control Systems programme, and in part by EPSRC grant' Launch and Recovery in Enhanced Sea States' (EP/P023002/1). This work was completed when Y. Zhang was with School of Engineering and Materials Science, Queen Mary University of London. Paper no. TSTE-00277-2020.
Publisher Copyright:
© 2010-2012 IEEE.
Keywords:
Excitation force estimation and prediction, M4, Non-causal linear optimal control (NLOC), Sliding mode compensator, Wave energy converter (WEC)
Identifiers
Local EPrints ID: 471509
URI: http://eprints.soton.ac.uk/id/eprint/471509
ISSN: 1949-3029
PURE UUID: 0784b470-e1ae-45a2-a596-92ac8c71e6b0
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Date deposited: 09 Nov 2022 18:11
Last modified: 24 Apr 2024 02:05
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Author:
Yao Zhang
Author:
Peter Stansby
Author:
Guang Li
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