PD-Based Optimal ADRC with Improved Linear Extended State Observer

Zhang, Zhen; Cheng, Jian; Guo, Yinan

doi:10.3390/e23070888

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…The optimization algorithm based on PSO [32][33][34][35][36][37][38][39][40] results in optimal design parameters for SMC and finding the optimal values of (𝑐 = 0.1), (𝐾 = 40) for ( 15) and (𝐾 1 = 6.93), (𝐾 2 = 600.59) for (17). The values of (𝐾 𝑝 ), (𝐾 𝑑 ), ( 𝛽 1 ), ( 𝛽 2 ) and (𝛽 3 ) are calculated according to (7) and (14) when choosing (𝑤 𝑐 = 24.5 𝑟𝑎𝑑/𝑠𝑒𝑐) and (𝑤 𝑜 = 4𝑤 𝑐 ).…”

Section: Numerical Simulation and Discussionmentioning

confidence: 99%

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Alawad¹,

Humaidi²,

Al-Araji³

2022

Journal of Robotics and Control

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Sliding mode (SM) has been selected as the controlling technique, and the state observer (SO) design is used as a component of active disturbance rejection control (ADRC) to reduce the knee position trajectory for therapeutic purposes. The suggested controller will improve the needed position performances for the Exoskeleton system when compared to the proportional-derivative controller (PD) and SMC as feed-forward in the ADRC approach, as shown theoretically and through computer simulations. Simulink tool is used in this comparison to analyze the nominal case and several disruption cases. The results of mathematical modeling and simulation studies demonstrated that SMC with a disturbance observer strategy performs better than the PD control system and SMC in feed-forward with a greater capacity to reject disturbances and significantly better than these controllers. Performance indices are used for numerical comparison to demonstrate the superiority of these controllers.

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Section: Numerical Simulation and Discussionmentioning

confidence: 99%

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Alawad¹,

Humaidi²,

Al-Araji³

2022

Journal of Robotics and Control

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“…(3) PD control with an improved linear extended state observer (PD-ILESO) [ 10 ]

where

is estimated by an improved linear ESO.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…In order to deal with the aforementioned negative factors and improve tracking performance, a large number of nonlinear control methods, such as backstepping control [ 3 , 4 ], sliding-mode control [ 5 , 6 , 7 , 8 ], and active disturbance rejection control [ 9 , 10 ], have been developed. Thanks to the simplicity and strong robustness of sliding-mode control, it has been widely used to cope with the uncertainty of the system [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Nonlinear Extended State Observer-Based Sliding-Mode Rotary Control for the Rotation System of a Hydraulic Roofbolter

Zhang

Guo

Song

2021

Entropy

Self Cite

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This paper develops a sliding-mode control with an improved nonlinear extended state observer (SMC-INESO) for the rotation system of a hydraulic roofbolter with dead-zones, uncertain gain, and disturbances, with the purpose of improving tracking performance. Firstly, the rotation system is modeled to compensate for dead-zone nonlinearity. Then, we present an improved nonlinear extended state observer to estimate disturbances of the rotation system in real time. Moreover, a proportional-integral-differential sliding-mode surface is introduced and an improved sliding-mode reaching law is designed. Based on this, a sliding-mode control law is developed. In order to eliminate the influence of estimation error and uncertain gain, we design two adaptation laws based on the sliding-mode surface and the estimated states. Finally, the effectiveness of the proposed SMC-INESO is verified through comparative simulation studies.

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“…Xu et al proposed a linear extended state observer with improvements by the adaptive design of the parameters for the controller using a particle swarm optimization algorithm. 2 Mosayyebi et al introduced the idea of fuzzy control and proposed a doubly-fed induction generator system with fuzzy fractional-order adaptive immunity control. 3 This method adapted well to changes in system parameters, and its stability and dynamic performance were greatly improved.…”

Section: Introductionmentioning

confidence: 99%

ADRC‐based optimized control system for wind turbine power generator

2023

Adv Control Appl

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As a renewable and clean energy source, wind energy can be converted into electricity by wind turbines. Currently, it is challenging to suppress the edgewise vibration of the wind turbine blades to improve the efficiency and service life of wind turbines. In this paper, active disturbance rejection control (ADRC)is applied to vibration suppression at the blade edge of wind turbines. First, the proposed system is mathematically modeled and analyzed in the frequency domain. Second, the ADRC is designed for the proposed system. Then, time domain analysis, frequency domain analysis, and uncertainty analysis of the closed‐loop system were carried out. It has been shown that the proposed controller has 12.2% less overshoot and 55.8% less setting time when subjected to perturbations than the PID controller. And the proposed controller has 33.9% less overshoot and 55.9% less setting time when subjected to perturbations than the SMC. Furthermore, the resonance frequency range of the proposed controller has been effectively reduced, and the peak resonance has been reduced by 41.7%. This reflects that the ADRC has a significant vibration suppression effect on blade edgewise vibrations compared with PID controllers and SMC.

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PD-Based Optimal ADRC with Improved Linear Extended State Observer

Cited by 11 publications

References 33 publications

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Improved Nonlinear Extended State Observer-Based Sliding-Mode Rotary Control for the Rotation System of a Hydraulic Roofbolter

ADRC‐based optimized control system for wind turbine power generator

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