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Description of Invention
A built-in self-scaling (BS) method is proposed and developed for classification of road roughness. The novelty of the BS method is in the way the DC gain is incorporated into the Bayesian formulation through the unconstrained least squares framework. It has a key feature of built-in self-scaling firstly with respect to the reliability of the measurements and secondly with respect to the number of measurements. The technique is applied to road classification by formulating a transfer function between the vehicle body acceleration as input and the suspension travel as output, capitalizing on its nonzero DC gain. The road roughness classification is achieved through comparison of the resulting frequency response magnitude patterns. This strategy leads to high classification accuracy requiring only low-cost sensors. It possesses a short detection time of 0.5s and a short training time of 5s for each road class. The method is model-free and does not require recalibration when the load carried by the vehicle changes. Additionally, it is capable of handling varying vehicle velocity and is effective for both passive and active suspensions, making it attractive for commercialization. A laboratory-scale experiment shows that the proposed technique increases the percentage of correct classification by an average of 34%, compared with a state-of-the-art method using augmented Kalman filtering. The significant improvement in the accuracy of road classification is impactful as it will enable controller design for suspension systems to be enhanced resulting in more comfortable ride and higher vehicle stability.
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Intellectual Property (IP) Status