Wu, Y, Li, S and Li, K orcid.org/0000-0001-6657-0522 (2018) Enhanced receding horizon optimal performance for online tuning of PID controller parameters. International Journal of Modelling, Identification and Control, 29 (3). pp. 209-217. ISSN 1746-6172
Abstract
In this paper, a new online proportional-integral-derivative (PID) controller parameter optimisation method is proposed by incorporating the philosophy of the model predictive control (MPC) algorithm. The future system predictive output and control sequence are first written as a function of the controller parameters. Then PID controller design is realised through optimising the cost function under the constraints on the system input and output. The MPC based PID online tuning easily handles the constraints and time delay. Simulation results in three situations, changing the control weight, adding constraints on the overshoot and control signal and changing the reference value, confirm that the proposed method is capable of producing good tracking performance with low energy consumption and short settling time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This paper is protected by copyright. This is an author produced version of a paper published in International Journal of Modelling, Identification and Control. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | online parameter optimisation; PID controller; model predictive control; MPC; tracking performance; control energy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2018 16:35 |
Last Modified: | 11 Apr 2019 00:43 |
Status: | Published |
Publisher: | Inderscience |
Identification Number: | 10.1504/IJMIC.2018.091239 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139076 |