An adaptive non-linear state estimator for vehicle lateral dynamics

Abstract

Artificial neural networks are used to estimate side slip angle and yaw rate of a vehicle's lateral dynamics. The networks are adapted to varying operating conditions such as a shift in vehicle weight, a change in road surface, and a radical change in tire characteristics. The structure and characteristics of the networks used are detailed. The methods for both offline and online training are described. Adaptation to the changing conditions is investigated with a high fidelity model and evaluated for ability and accuracy. A method of reducing computational burden while preserving model generalization is described. Model accuracy and generalization are examined to evaluate the networks' ability to describe general vehicle behavior. Improvement in estimate error of 3 to 1 and nearly 300 to 1 for two typical scenarios is demonstrated.

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

“…In another promising work, Broderick et al [104] trained an ANN with a set of manoeuvres in order to take into account a shift in vehicle weight, a change in road surface, and a radical change in tyre characteristics. However, the training procedure is highly time demanding, and they only tested the network on two scenarios.…”

“…Neural network-based [96][97][98][99][100][101][102][103][104][105][106][107][108][109][110]: This method is specifically used to overcome the need for a vehicle model of any kind and its related complex set of parameters [101]. Artificial neural networks (ANN) are largely considered effective tools for system modelling, as they are suitable to model complex systems using their ability to identify relationships from input-output data pairs.…”