Ty-Jour A2 - Bukovsky，Ivo Au - Kramer，Kathleen A. Au - Stubberud，Stephen C. Py - 2011 DA - 2011/12/22 TI - 控制回路传感器校准使用神经网络进行机器人控制SP - 845685 VL - 2011AB - Whether sensor model’s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon the sensor’s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous navigation. A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a control loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple sensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural network on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into the system to provide the added estimation capability and redundancy. SN - 1687-9600 UR - https://doi.org/10.1155/2011/845685 DO - 10.1155/2011/845685 JF - Journal of Robotics PB - Hindawi Publishing Corporation KW - ER -