Tuning of active disturbance rejection control for differentially flat systems under an ultimate boundedness analysis: a unified integer-fractional approach

Otero-Leal, Jeisson E.; Cortés‐Romero, John; Aguilera, Efredy Delgado; Galarza-Jimenez, Felipe; Jimenez-Triana, Alexander

doi:10.1007/s11768-021-00037-z

Cited by 2 publications

(1 citation statement)

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“…The core idea of active disturbance rejection control (ADRC) is to treat the uncertainty, model error, interference, etc. in the system as an expanded state, which is estimated and compensated by the observer (Otero-Leal et al , 2021; Hou et al , 2020; Monfaredi et al , 2014). Compared with the traditional PID controller, it does not depend on the precise mathematical model of the system, only requires a small amount of system information and has the characteristics of a fast response, small overshoot, strong robustness and simple structure.…”

Section: Introductionmentioning

confidence: 99%

Parameter tuning of auto disturbance rejection controller based on improved glowworm swarm optimization algorithm

Gao

Shen

Dai

et al. 2022

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Purpose This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications. Design/methodology/approach This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters. Findings The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short; there is no overshoot; the tracking process is relatively stable; the anti-interference ability is strong; and the optimization effect is better. Originality/value The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

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Section: Introductionmentioning

confidence: 99%