Classical and intelligent methods in model extraction and stabilization of a dual-axis reaction wheel pendulum: A comparative study

Bezci, Yüksel Ediz; Aghaei, Vahid Tavakol; Akbulut, Batuhan Ekin; Tan, Deniz; Allahviranloo, Tofigh; Fernandez-Gamiz, Unai; Noeiaghdam, Samad

doi:10.1016/j.rineng.2022.100685

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…01 231 245 ÿ 37 28ÿ 9 2 Note that both controllers were designed based on the root locus method. As shown in Figure 9, using state-space representation (7), the pole-zero maps of the pendulum were derived with respect to the input signal u.…”

Section: Cascade Pid Controlmentioning

confidence: 99%

“…The controller design was realised using a microcontroller, which is a digital device. Therefore, the state-space model (7) was discretised. For that purpose, the zero-order hold method was employed.…”

Section: Linear-quadratic-gaussian Controllermentioning

confidence: 99%

“…A neural network-based control approach was introduced in [6]. Yet another method that involves fuzzy controllers was proposed in [7]. Similarly, the authors of [8] developed a fuzzy controller and investigated its performance by comparing it with an LQR controller.…”

Section: Introductionmentioning

confidence: 99%

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Design, Implementation, and Control of a Wheel-Based Inverted Pendulum

Zaborniak,

Patan,

Witczak

2024

Electronics

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Control of an inverted pendulum is a classical example of the stabilisation problem pertaining to systems that are unstable by nature. The reaction wheel and the motor act as actuators, generating the torque needed to stabilise the system and counteract inevitable disturbances. This paper begins by describing the design and physical implementation of a wheel-based inverted pendulum. Subsequently, the process of designing and testing the proportional–integral–derivative (PID) and unknown input Kalman-filter-based linear quadratic regulator (LQR) controllers is performed. In particular, the design and pre-validation were carried out in the Matlab/Simulink environment. The final validation step was realised using a constructed physical pendulum, with a digital controller implemented using the STM32 board. Finally, a set of various physical disturbances were introduced to the system to show the high reliability and superiority of the proposed Kalman-filter-based LQR strategy.

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Section: Cascade Pid Controlmentioning

confidence: 99%

Section: Linear-quadratic-gaussian Controllermentioning

confidence: 99%

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Design, Implementation, and Control of a Wheel-Based Inverted Pendulum

Zaborniak,

Patan,

Witczak

2024

Electronics

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“…Among these control strategies, PID can be considered as the simplest one with three parameters to be tuned for optimal performance, but when it comes to controlling nonlinear complicated systems traditional PID controllers cannot perform well enough [17] . To conquer this, some other classical methods like linear quadratic regulator (LQR) and state feedback can be beneficial [3] .…”

Section: Introductionmentioning

confidence: 99%