Real time estimation of Kalman filter parameters using the genetic algorithm for optimum balancing controller of two-wheel robotic system

Joint Authors

Aula, Fadil T.
Tawfiq, Lina N.

Source

ZANCO Journal of Pure and Applied Sciences

Publisher

Salahaddin University-Erbil Department of Scientific Publications

Publication Date

2016-06-30

Country of Publication

Iraq

No. of Pages

6

Main Subjects

Information Technology and Computer Science

English Abstract

In this paper, a real time estimation procedure is presented in a self-balancing two-wheel robotic system.

Such a robotic system has a potential problem due to its unstable state for controlling the balance while the robot is moving forward and/or backward on two-wheel.

The controller includes two subsystems: selfbalance, which prevents the system from falling down when it moves, and yaw rotation, which regulates a wheel angle when it turns right and left.

The genetic algorithm (GA) is used for estimating and tuning Kalman parameters : measurement noise covariance, R, and process noise covariance, Q.

The twowheel robotic system that has been used in this research is adapted with Inertial Measurement Unit (IMU) sensor.

The algorithm is simulated in MATLAB software and the results have shown the effectiveness of GA for getting the optimum tuning of Q and R for the self-balancing robotic system.

Data Type

Conference Papers

Record ID

BIM-787468

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