Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.

Assessing the relative road damaging potential of HGVs

Assessing the road damaging potential of heavy vehicles is becoming an increasingly important issue. In this paper, current vehicle regulations and possible future alternatives are reviewed, and are categorized as tests on individual axles and whole vehicles, and 'direct' and 'indirect' tests. Whole vehicle methods of assessing road damaging potential accurately are then discussed. Direct methods are investigated (focussing on using a force measuring mat), and drawbacks are highlighted. Indirect methods using a transient input applied to individual axles are then examined. Results indicate that if non-linearities are accounted for properly, indirect methods of assessing whole vehicle road damaging potential could offer the required accuracy for a possible future test procedure.