SVM Classification and Kalman Filter Based Estimation of the Tire-Road Friction Curve

Author:

Regolin Enrico,Ferrara Antonella

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference17 articles.

1. A recursive algorithm for nonlinear least-squares problems;Alessandri;Computational Optimization and Applications,2007

2. Adaptive emergency braking control with observer-based dynamic tire/road friction model and underestimation of friction coefficient;Alvarez;Journal of Dynamic Systems Measurement and Control,2005

3. de Castro, R., Araujo, R.E., Cardoso, J.S., and Freitas, D. (2010). A new linear parametrization for peak friction coefficient estimation in real time. In Proceedings of the IEEE Vehicle Power and Propulsion Conference. Lille, France.

4. Support vector machines for classification and regression. techreport;Gunn,1998

5. Adaptive unscented kalman filter and its applications in nonlinear control;Han,2009

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1. A Mind Evolutionary Algorithm Optimized Back-Propagation Neural Network Model for Tire-Road Friction Coefficient Prediction;Lecture Notes in Electrical Engineering;2024

2. Data-driven and uncertainty-aware robust airstrip surface estimation;Neural Computing and Applications;2023-06-29

3. The tire slip angle estimation algorithm based on intelligent tire technology;Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering;2023-01-12

4. Neural Network-Based Road Surface Condition Monitor Concept;Applied Condition Monitoring;2023

5. Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system;2021 29th Mediterranean Conference on Control and Automation (MED);2021-06-22

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