A design for two-wheeled self-balancing robot based on Kalman filter and LQR

Sun, Fengxin; Yu, Zhen; Yang, Haijiao

doi:10.1109/icmc.2014.7231628

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…Data from gyroscope and accelerometer is fused using (1) to (7), and from Sun et al (2014), we model the states as:…”

Section: Q-learning With Kalman Filtermentioning

confidence: 99%

“…where Φ is the pitch angle state of gyro, gyro is the pitch angle measured by gyro, g e is the drift error by gyro, g is the measurement noise of gyro, acce is the pitch angle by accelerometer (Sun et al, 2014), T x is the sampling time period to discretize Kalman filter from continuous space, for T x where x is in ( gyro , g , g e ) , and K is the Kalman gain (Docs.ros.org., 2021); for IMU sensors, we used gazebo IMU noise model in ROS and set sensor update frequency to 1000 Hz, and thus, we set T x = 1ms . Further, we set noise parameter for angular rates (gyro) and linear acceleration ( 8)…”

Section: Q-learning With Kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

Srichandan

Dhingra

Hota

2021

J Control Autom Electr Syst

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A two-wheeled self-balancing robot (SBR) is a typical example in control systems that works on the principle of an inverted pendulum. In this paper, we experiment to see how the learning and stability performance varies based on Kalman filter introduction for IMU noise filtering and controlling the robot using reinforcement learning. All the implementation is performed in ROS and Gazebo, and Q-learning is implemented using OpenAI (toolkit for development of Reinforcement learning) for ROS, i.e., Openai_ros package. Our work deals with a novel approach of providing the angular output from IMU to Kalman filter and passing it to the input of Q-learning for balancing control. Finally, we analyze the results with and without using Kalman filter from the output of IMU before passing it to Q-learning and evaluate the performance based on robot's learning behavior and its robustness.

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“…Data from gyroscope and accelerometer is fused using (1) to (7), and from Sun et al (2014), we model the states as:…”

Section: Q-learning With Kalman Filtermentioning

confidence: 99%

Section: Q-learning With Kalman Filtermentioning

confidence: 99%

An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

Srichandan

Dhingra

Hota

2021

J Control Autom Electr Syst

View full text Add to dashboard Cite

show abstract

“…When the TWIP robots work at a small pitch angle around the balancing point, some conventional linear control techniques such as PID control [8][9][10][11] and linear quadratic regulator (LQR) [8][9] [12][13][14][15][16] have been employed. However, when the robot operates in nonlinear regions with large pitch angles because of external disturbances, modeling errors, or internal maneuvers, the control performance of the linear control approaches will be degraded.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

Tran,

Hoang,

Loc

et al. 2023

Journal of Robotics and Control

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This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture.

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“…The Kalman filtering algorithm was employed to reduce or eliminate several error signals from the output of the sensor [10]. The PID control algorithm was used to regulate the forward, backward, rotating, and stabilizing perpendicular movement to the flat plane, also to rotate the direction of the robot [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Two-Wheeled Balancing Mobile Robot with PID Controller

Mudeng¹,

Hassanah²,

Priyanto³

et al. 2020

IJSTT

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Mobile transportation robots using two wheels have now been investigated. The work within this study is to design and simulate two-wheeled robots, thus it can maintain its balance. Many control methods are used to determine satisfactory control optimization, therefore a proper response is obtained by sensor recitation corresponding with the reaction of a Direct Current (DC) motor. Recently, two-wheeled transportation robot is a Segway model. In this study, we apply a Proportional Integral Derivative (PID) controller as a control system in a self-balancing robot with a working principle is similar to an inverted pendulum. In the next study, the PID controller and the whole system are applied in the microcontroller board. The angular velocity of two DC motors used as a plant can be adjusted by Pulse Width Modulation (PWM) through a motor driver. An Inertial Measurement Unit (IMU) sensor is utilized to detect the angular acceleration and angular velocity of the self-balancing robot. The phase design is constructed by planning the robot dimension, mechanical system, and an electronic system. Particularly, this study performs mathematical modeling of the robot system to obtain the transfer function. In addition, we simulate the PID parameter with multiplication between the basic parameter and several fixed constants. The simulation results indicate that the robot can maintain its balance and remains perpendicularly stable for balancing itself.

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A design for two-wheeled self-balancing robot based on Kalman filter and LQR

Cited by 16 publications

References 8 publications

An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

Design and Simulation of Two-Wheeled Balancing Mobile Robot with PID Controller

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