Article: An improved fuzzy logic-based vehicle dynamics state estimation framework via coordination of extended Kalman filter and artificial neural networks Journal: International Journal of Vehicle Performance (IJVP) 2025 Vol.11 No.4 pp.379 - 409 Abstract: This paper proposes a 'combined estimation algorithm (CEA)' designed to estimate the unknown yaw rate and side-slip angle values of a tactical vehicle while navigating at various speeds during a NATO double lane change manoeuvre, using known states. The CEA includes three independent modules: an extended Kalman filter (EKF), artificial neural networks (ANN), and fuzzy logic (FL). The EKF is based on a single-track nonlinear vehicle model, while the ANN using trained data. Both modules take the front axle steering angle as their single input and operate simultaneously in coordination with a novel fuzzy logic (FL) module, which integrates the outputs of both the ANN and EKF, utilising practical rules and membership functions derived from experience and experimental data to enhance prediction performance. Simulations demonstrated that the proposed CEA improves the estimation accuracy by between 8% and 59% for all error metrics, compared to the EKF and ANN alone. Inderscience Publishers - linking academia, business and industry through research

Title: An improved fuzzy logic-based vehicle dynamics state estimation framework via coordination of extended Kalman filter and artificial neural networks

Authors: Volkan Bekir Yangin; Yaprak Yalcin; Ozgen Akalin

Addresses: Department of Mechanical Engineering, Faculty of Mechanical Engineering, Istanbul Technical University, Beyoglu, Istanbul, 34437, Türkiye ' Department of Control and Automation Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Sariyer, Istanbul, 34469, Türkiye ' Department of Mechanical Engineering, Faculty of Mechanical Engineering, Istanbul Technical University, Beyoglu, Istanbul, 34437, Türkiye

Abstract: This paper proposes a 'combined estimation algorithm (CEA)' designed to estimate the unknown yaw rate and side-slip angle values of a tactical vehicle while navigating at various speeds during a NATO double lane change manoeuvre, using known states. The CEA includes three independent modules: an extended Kalman filter (EKF), artificial neural networks (ANN), and fuzzy logic (FL). The EKF is based on a single-track nonlinear vehicle model, while the ANN using trained data. Both modules take the front axle steering angle as their single input and operate simultaneously in coordination with a novel fuzzy logic (FL) module, which integrates the outputs of both the ANN and EKF, utilising practical rules and membership functions derived from experience and experimental data to enhance prediction performance. Simulations demonstrated that the proposed CEA improves the estimation accuracy by between 8% and 59% for all error metrics, compared to the EKF and ANN alone.

Keywords: extended Kalman filter; EKF; artificial neural networks; ANN; fuzzy logic; FL; state estimation; vehicle dynamics.

DOI: 10.1504/IJVP.2025.149506

International Journal of Vehicle Performance, 2025 Vol.11 No.4, pp.379 - 409

Received: 07 Mar 2025
Accepted: 29 Jul 2025
Published online: 04 Nov 2025 *

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Title: An improved fuzzy logic-based vehicle dynamics state estimation framework via coordination of extended Kalman filter and artificial neural networks

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