Hybrid State Estimation–A Contribution Towards Reliability Enhancement of Artificial Neural Network Estimators

Abstract

Data-driven models are obtained purely from data without complex theoretical modeling and without explicit model knowledge. This results in black box models whose traceability and reliability constitute a major challenge. This contribution addressed this issue and presents a novel hybrid estimation method, which enhances the reliability of artificial neural networks. Within this method, a simple model based on physical knowledge secures a model based on an artificial neural network. An unscented Kalman filter realizes the interaction of the two individual models. Thereby, a confidence level determines which model is trusted to a greater extent or even entirely. As part of the method for adjusting this confidence level, the input variables of the artificial neural network are related to the data used in training. The more often the artificial neural network has encountered a situation in the training process, the greater the confidence level will be. Finally, the confidence level is used to set the covariances of the unscented Kalman filter. In this contribution, the method is presented using the application of roll angle estimation for passenger cars. By using the hybrid method the reliability of the estimation is increased in comparison to the artificial neural network. For this purpose, sensor malfunctions as well as a sensor failure are simulated. These disturbances are compensated by the introduced method. In addition, the hybrid state estimator increases the estimation quality compared to the individual estimators. The proposed method can be applied to any problem, where knowledge-based models are available to secure data-driven models.