Title: Enhanced receding horizon optimal performance for online tuning of PID controller parameters

Authors: Yongling Wu; Shaoyuan Li; Kang Li

Addresses: Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China ' Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China ' School of Electronics, Electrical Engineering and Computer Science, Queens University Belfast, Belfast BT9 5AH, UK

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.

Keywords: online parameter optimisation; PID controller; model predictive control; MPC; tracking performance; control energy.

DOI: 10.1504/IJMIC.2018.091239

International Journal of Modelling, Identification and Control, 2018 Vol.29 No.3, pp.209 - 217

Received: 06 Jan 2017
Accepted: 08 Mar 2017

Published online: 11 Apr 2018 *

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