Title: Performance optimisation of discrete time linear active disturbance rejection control approach

Authors: Congzhi Huang; Bin Du; Chaomin Luo

Addresses: School of Control and Computer Engineering, North China Electric Power University, Beijing, China; Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, Guangxi, China ' State Grid Sichuan Electric Power Corporation Metering Center, No. 18, Wanjing 1st Road, Wuhou District, Chengdu, China ' Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, USA

Abstract: In the framework of the linear active disturbance rejection control (LADRC) approach, all the uncertainties, including the perturbed internal model parameters and time-varying external disturbances, can be estimated by constructing an extended state observer, and then cancelled in real time. However, the parameter tuning of the approach is an extremely challenging mission. In this paper, the model parameters of the controlled servo motor control system are identified by employing the algebraic parameter identification approach. Afterward, the bacteria foraging optimisation (BFO) algorithm, and the particle swarm optimisation (PSO) algorithm are both proposed to optimise the performance of the system driven by the LADRC approach in light of the identified model of the servo motor. The BFO and PSO algorithms and LADRC approach have been extensively applied in optimised control and networked systems. Extensive simulation results and experimental tests are given to demonstrate that the proposed approaches are effective and efficient for the performance optimisation of the LADRC approach.

Keywords: algebraic parameter identification; bacteria foraging optimisation; BFO; discrete time; LADRC; particle swarm optimisation; PSO; performance optimisation.

DOI: 10.1504/IJAAC.2020.110070

International Journal of Automation and Control, 2020 Vol.14 No.5/6, pp.713 - 733

Received: 09 Sep 2018
Accepted: 20 Feb 2019
Published online: 05 Oct 2020 *

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