Dynamic Modeling of a Cubical Robot Balancing on Its Corner

Chen, Zhigang; Ruan, Xiaogang; Li, Yuan

doi:10.1051/matecconf/201713900067

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…) Thus, the reaction torques on the cube along the axes of body coordinate frame are denoted by α τ , β τ and γ τ [6], that is…”

Section: Dynamics Modelingmentioning

confidence: 99%

“…The STM32 controls the rotation speed of the motor according to the attitude angle of the cube and acts on the rotation of the reaction wheels. The forward or reverse driving torque of the entire mechanism, thus realizing the balance of the mechanism with the edge as the fulcrum or the angle as the fulcrum [6,7], is a typical non-linear, unstable multi-freedom spatial inverted pendulum system.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Ruan¹,

Li²,

Chen³

2018

dtcse

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In this paper, a cubic self-balancing robot is designed. Taking a prototype of a cubic robot that has been designed as a specific research object. For the attitude control of this institution. Using Lagrangian Method to establish a mathematical model of cubic robot. For its attitude control method. Fuzzy PID control method was proposed. Establishing simulation models in Matlab/Simulink environment. The simulation experiments using conventional PID control and fuzzy PID control were performed for comparison. The results show that the adoption of fuzzy PID control has faster response speed and smaller overshoot than traditional PID control and has better control effect.

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“…) Thus, the reaction torques on the cube along the axes of body coordinate frame are denoted by α τ , β τ and γ τ [6], that is…”

Section: Dynamics Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Ruan¹,

Li²,

Chen³

2018

dtcse

Self Cite

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“…Other described control elements are flywheels which can be used in different ways. The velocity of the flywheels can be controlled [7], [8], which affects production of a specified amount of torque acting on the frame. This approach uses the principle of conservation of angular momentum.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the selected way of controlling the movement of the cube, there are different approaches to modelling the balancing system. The system can be modelled as a three-dimensional system described with positions in X, Y, and Z axes and respective angles: pitch, yaw, and roll [6], [7], [8]. The other way is taking into account that when using the principle of conservation of energy and modelling with Euler Lagrange equations, the movement of one face of the cube does not affect the other faces.…”

Section: Introductionmentioning

confidence: 99%

Optimal State Feedback Controller for Balancing Cube

Piotrowski,

Kowalczyk

2023

JAMRIS

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In this paper, a nonlinear balancing cube system is considered which concept is based on an inverted pendulum. The main purpose of this work was the modelling and construction of a balancing cube with the synthesis of the control system. The control objectives included swing-up and stabilization of the cube on its vertex at an unstable equilibrium. Execution of the intended purpose required, firstly – deriving a cognitive mathematical model. It was based on the Lagrange method. Next, a mathematical model for control purposes was derived. The project of the physical model of the balancing cube was presented. A stabilization system based on a linear quadratic regulator (LQR) was developed. Moreover, a swing-up mechanism was used to bring the cube close to the upper equilibrium point. The algorithm switching condition was important to enable the correct functioning of the system. The developed control system was verified in the Matlab environment. Finally, verifying experiments and comparison between models (mathematical and physical) were performed.

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“…The linear-quadratic regulator (LQR) control algorithm for the Cubli balancing on its edge and corner was conducted in [2]. In 2017, Chen et al [3] proposed methods to construct the dynamic model of a self-balancing cube robot and a proportional-integral-derivative (PID) controller was proposed to accomplish the action of the cube robot balancing [4]. However, the cube robot mentioned above did not provide the dynamic model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics and Control of a Cube Robot

et al. 2020

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The paper aims to solve problems of the mathematical modeling and realization of a cube robot capable of self-bouncing and self-balancing. First, the dynamic model of the cube robot is derived by using the conservation of the angular momentum and the torque equilibrium theory. Furthermore, the controllability of the cube robot is analyzed and the angle of the cube robot is derived from the attitude and heading reference system (AHRS). Then the parallel proportional–integral–derivative (PID) controller is proposed for the balancing control of the self-designed cube robot. As for the bounce control of the cube robot, a braking system triggered by the servo motor is designed for converting the kinetic energy to the potential energy. Finally, the experimental results are included to demonstrate that the cube robot can complete the actions of self-bouncing and self-balancing with good robustness to external disturbances.

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Dynamic Modeling of a Cubical Robot Balancing on Its Corner

Cited by 8 publications

References 9 publications

Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Optimal State Feedback Controller for Balancing Cube

Nonlinear Dynamics and Control of a Cube Robot

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