Experimental Comparison of The Two Most Used Vehicle Sideslip Angle Estimation Methods for Model-Based Design Approach. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Chindamo, Daniel; Gadola, Marco; Bonera, Emanuele; Magri, Paolo
• Is Part Of: Journal of physics. Volume 1888:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1888/1/012006
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Vehicle sideslip angle estimation is still one of the most challenging research topics in the automotive industry. Many papers can be found on this topic, where authors propose varied methods to reach the goal. Which is the most effective? After an extensive literature review, two very different methods have been identified as the most used: Extended Kalman Filter with dynamic model and Artificial Neural Network. In this work a comparison among these methods is presented. A fully instrumented car has been used to gather typical vehicle dynamics data and feed the models required for a model-based design approach. Results showed that each method has either positive aspects or drawbacks.