System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks

Chandran, Deepak; Krishna, Bipin; George, V I; Thirunavukkarasu, I

doi:10.1109/iic.2015.7150815

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Currently, a few studies are conducted on RDPIP to calculate its dynamic equation and investigate for stability [13] [14]. In addition, some controllers are also deployed on RDPIP to control and maintain the stability of the system, such as the LQR controller [15] [16] [17] [18], and the neural networks controller [19]. The main objective of this investigation is to develop a mathematical model of RDPIP and survey some linear controllers, such as LQR controller, PID-LQR controller, and cascade PID-LQR controller for RDPIP.…”

Section: Introductionmentioning

confidence: 99%

Linear Control Schemes for Rotary Double Inverted Pendulum

Vo,

Nguyen,

Huynh

et al. 2023

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In this article, the rotary double parallel inverted pendulum (RDPIP) is researched and presented. Additionally, some linear controllers, such as PID, PID, PID-LQR cascade, are proposed, developed to control RDPIP, and the impact of those controllers on the rotary double inverted pendulum was examined. The research and simulation results are implemented in the Matlab/Simulink toolbox to prove the ability of these types of controller in balancing this model.

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Section: Introductionmentioning

confidence: 99%

Linear Control Schemes for Rotary Double Inverted Pendulum

Vo,

Nguyen,

Huynh

et al. 2023

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“…[19] employs a fuzzy PD controller for hold mode; and a PD controller in the swing-up mode of operation. Neural Networks are also used both to control [20] and to identify [21] RIP. A scheme that utilizes the nonlinear adaptive neural network control method is established in [22] for the hold mode of operation.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Stabilization of Rotary Inverted Pendulum by State Feedback

ŞEN

Beldek

2022

Preprint

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A Rotary Inverted Pendulum is a standard test-bed in control theory due to its nonlinear and unstable characteristics. Driving the pendulum to an upright position, sustaining it motionless there, and carrying on signal tracking tasks at this position are the main control applications related to this test device. This paper presents a comparative study of four state feedback controller structures performing pole placement that theoretically stabilizes the pendulum at an upright position and in real-time application. The first state feedback controller is established based solely on the pendulum dynamics. In contrast, the other controllers are developed due to a multi-criteria optimization procedure by running a Genetic Algorithm search. The Quanser kit is used to obtain and implement the controllers and to test their performance in real-time.

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