High precision stabilization of pan-tilt systems using reliable angular acceleration feedback from a master-slave Kalman filter
Evren Han, Sanem and Yavuz, Fırat and Ünel, Mustafa (2017) High precision stabilization of pan-tilt systems using reliable angular acceleration feedback from a master-slave Kalman filter. Journal of Intelligent & Robotic Systems . ISSN 0921-0296 (Print) 1573-0409 (Online) Published Online First http://dx.doi.org/10.1007/s10846-017-0522-9
Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s10846-017-0522-9
Pan-tilt robotic platforms are widely used to point sensor arrays (cameras, radars and antennas,i.e.) in certain directions toward the target. Such platforms may suffer from external disturbances due to terrain changes, high-frequency vibrations and sudden shocks, wind and other environmental factors. These disturbances and hence small angular displacements may cause large position errors if the target is too far away. Therefore, high precision stabilization is required for these platforms to converge to the desired orientation and maintain it by rejecting unknown disturbances. In motion control, robust stabilization problem is usually tackled by employing angular acceleration feedback. However, obtaining reliable acceleration information is a challenging task. In this paper, we propose a novel master-slave type Kalman filter algorithm which consists of an extended Kalman filter (EKF) and an inverse Phi-algorithm in a master-slave configuration to estimate reliable angular acceleration signals by fusing 3-axis gyroscope, 3-axis accelerometer and 3-axis magnetometer data. Performance of the proposed sensor fusion algorithm is evaluated on a high fidelity simulation model by using the estimated accelerations as feedback signals in the stabilization control of a 2-DOF pan-tilt system subject to external disturbances. As the acceleration feedback is incorporated into the control loop, higher stabilization is achieved. The performance of the proposed fusion algorithm is compared to Newton predictor enhanced Kalman filter (NPEKF) and the error state Kalman filter (ErKF). The proposed master-slave Kalman filter outperforms the Newton predictor enhanced Kalman filter whereas the results obtained by the proposed algorithm and the error state Kalman filter are comparable.
Repository Staff Only: item control page