Comparison of two efficient control strategies for two-wheeled balancing robot

Majczak, Michal; Wawrzyński, Paweł

doi:10.1109/mmar.2015.7283968

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…The relationship between the displacements of the wheel along the x-axis and the rotational angle of the wheel about the y-axis is: (10) On the other hand, the relationship between the heading angle ( ) of the robot about the z-axis and the displacement of the wheel along the x-axis is: (11) By subtracting (1) and (2) from (3) and (4) respectively then substituting it to (11) result in:…”

Section: Research Methods 21 Mathematical Modeling Of the Robotmentioning

confidence: 99%

“…In [6], dynamics was derived using a Newtonian approach and the control was design by the equations linearized around an operating point. In [7][8][9], and [10] a linear stabilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controller was derived by a planar model without considering robot's heading angle. The above control law was designed by a planar model without considering robot's heading angle therefore still cannot be implemented into a real system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

Asali¹,

Hadary²,

Sanjaya³

2017

IJECE

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Two-wheeled self-balancing robot is a popular model in control system experiments which is more widely known as inverted pendulum and cart model. This is a multi-input and multi-output system which is theoretical and has been applied in many systems in daily use. Anyway, most research just focus on balancing this model through try-on experiments or by using simple form of mathematical model. There were still few researches that focus on complete mathematic modeling and designing a mathematical model based controller for such system. This paper analyzed mathematical model of the system. Then, the authors successfully applied a Linear Quadratic Regulator (LQR) controller for this system. This controller was tested with different case of system condition. Controlling results was proved to work well and tested on different case of system condition through simulation on matlab/Simulink program.

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Section: Research Methods 21 Mathematical Modeling Of the Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

Asali¹,

Hadary²,

Sanjaya³

2017

IJECE

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“…Since such balancing robot is a nonlinear system with nonholonomic constraints and inherent unstability, several control methods have been proposed for effective control. Among them, the control of a balancing robot through PID control is most basic and general control method [4], [5], [6]. However, many trials and errors and much time are needed in the process of tuning gains, and whenever the system is changed, new gains should be found.…”

Section: Introductionmentioning

confidence: 99%

Controller Implementation of a Balancing Robot through a Dynamic Model with Acceleration Control Input

Choi¹,

Jo²,

Lee³

2017

World Congress on Mechanical, Chemical, and Material Engineering

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In this paper, we propose a new dynamic model of the balancing robot, and present the implementation method and results of the controller based on this model. The model equation of the system is derived through the Lagrangian approach. The model for tracking control of balancing robot is proposed, of which tracking reference is added as state, and implemented through LQR controller. The dynamic model proposed in this paper has the acceleration of robot body as the control input, unlike the conventional model which uses the torque or voltage as the control input. The acceleration from the LQR controller is transformed to the velocity reference of a motor and this value is applied to the real system through motor velocity controller. The motor velocity controller could be separated from the LQR controller making control reference. Therefore, the entire control could be easily implemented by changing only the motor velocity controller depending on the motor used in a balancing robot. The performance of the entire controller can be improved by controlling velocity accurately. To verify usefulness and reliability of this proposed model, two balancing robots, one of which uses step motors and the other of which uses DC motors, are implemented and results for balancing and tracking control are presented.

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“…A comparison between two methods is presented by [8]. The first method is a cascade of a PI controller and a mathematical model of the robot.…”

Section: Introductionmentioning

confidence: 99%

Velocity and Motion Control of a Self-Balancing Vehicle Based on a Cascade Control Strategy

Velazquez

Cruz

García

et al. 2016

International Journal of Advanced Robotic Systems

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This paper presents balancing, velocity and motion control of a self-balancing vehicle. A cascade controller is implemented for both balancing control and angular velocity control. This controller is tested in simulations using a proposed mathematical model of the system. Motion control is achieved based on the kinematics of the robot. Control hardware is designed and integrated to implement the proposed controllers. Pitch is kept under 1° from the equilibrium position with no external disturbances. The linear cascade control is able to handle slight changes in the system dynamics, such as in the centre of mass and the slope on an inclined surface.

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Comparison of two efficient control strategies for two-wheeled balancing robot

Cited by 8 publications

References 6 publications

Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

Controller Implementation of a Balancing Robot through a Dynamic Model with Acceleration Control Input

Velocity and Motion Control of a Self-Balancing Vehicle Based on a Cascade Control Strategy

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