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

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 accor...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.2743-2753
Main Authors: You, Sesun, Son, Young Seop, Gui, Yonghao, Kim, Wonhee
Format: Article
Language:English
Subjects:
Backstepping
Closed loops
Control systems design
Controllers
Cost analysis
Cost function
D C motors
disturbance observer
disturbance observer (DOB)
Disturbance observers
Electric motors
ENGINEERING
Feedback control
Gradient methods
gradient-descent
input-to-state stability
input-to-state stability (ISS)
Learning
Learning control
Learning systems
Mathematical analysis
Nonlinear systems
Perturbation methods
Stability
Stability analysis
Stability criteria
Tuning
Upper bound
Upper bounds
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3234439