Control loop sensor calibration using neural networks

Abstract

Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system to become unstable. Whether the sensor model's inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental. In this paper 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 in to the system to provide the added estimation capability and redundancy. ©2008 IEEE.