Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

Sesun You; Young Seop Son; Yonghao Gui; Wonhee Kim

doi:10.1109/ACCESS.2023.3234439

Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

Sesun You, Young Seop Son, Yonghao Gui, Wonhee Kim

Grid Systems Modeling and Controls

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-To-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

Original language	English
Pages (from-to)	2743-2753
Number of pages	11
Journal	IEEE Access
Volume	11
DOIs	https://doi.org/10.1109/ACCESS.2023.3234439
State	Published - 2023

Funding

This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2021R1A6A1A03043144; in part by the Technology Innovation Program (Development of Integrated Minimal Risk Maneuver Technology for Fallback System During Autonomous Driving) through the Ministry of Trade, Industry and Energy (MOTIE), South Korea, under Grant 20014121; in part by the Development of Electromechanical Brake Booster System for High-Efficiency Regenerative Braking of Micro-Electric Vehicles and Light Vehicles through MOTIE under Grant 20020728; in part by NRF Grant through the Korean Government, Ministry of Science and ICT (MSIT), under Grant 2022R1F1A1075479; and in part by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office Program, under Contract DE-AC05-00OR22725.

Funders	Funder number
U.S. Department of Energy
Solar Energy Technologies Program	DE-AC05-00OR22725
Office of Energy Efficiency and Renewable Energy
Ministry of Education	2021R1A6A1A03043144
Ministry of Trade, Industry and Energy	20014121, 20020728
Ministry of Science, ICT and Future Planning	2022R1F1A1075479
National Research Foundation of Korea

Keywords

Learning control
disturbance observer (DOB)
gradient-descent
input-To-state stability (ISS)

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10.1109/ACCESS.2023.3234439

Cite this

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title = "Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems",

abstract = "This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-To-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.",

keywords = "Learning control, disturbance observer (DOB), gradient-descent, input-To-state stability (ISS)",

author = "Sesun You and Son, {Young Seop} and Yonghao Gui and Wonhee Kim",

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T1 - Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

AU - You, Sesun

AU - Son, Young Seop

AU - Gui, Yonghao

AU - Kim, Wonhee

PY - 2023

Y1 - 2023

N2 - This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-To-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

AB - This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-To-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

KW - Learning control

KW - disturbance observer (DOB)

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KW - input-To-state stability (ISS)

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JF - IEEE Access

ER -

Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

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