Abstract—To adapt to these new conditions, this movement towards safe, accurate, and high-speed (ease of operation) control for autonomous vehicles requires adaptive control strategies that adjust for nonlinear dynamics and unpredictable operating conditions. This paper proposes an intelligent control framework for Ackermann-type automated vehicles based on a Neural Network-Optimized Fuzzy Proportional-Integral-Derivative (NN-FPID) controller to circumvent the shortcomings of traditional PID and fixed-rule Fuzzy-PID controllers. Using a lightweight neural network, the fuzzy membership functions and PID gains can be continuously and automatically updated based on the vehicle’s steering error, velocity error, and lateral deviation. The Ackermann vehicle model is generated in Simscape Multibody, enabling simulation of kinematic and dynamic behavior with realistic components for vehicle control. Compared to the control controller, the new NN-FPID controller reduces Root Mean Square Error (RMSE) for lateral tracking by 50–60%, overshoot by up to 65%, and settling time by approximately 55% across straight roads, turns, zigzags, bumps, and other road conditions. The average time per control step is less than 1 ms, supporting the deployment of this technique for real-time implementation in embedded autonomous driving systems.

Keywords—fuzzy-Proportional–Integral–Derivative (PID), Neural Network-Optimized Fuzzy Proportional-Integral- Derivative (NN-FPID), Ackermann, autonomous vehicles

Cite: Thanh-Ha Vo, Thi-Thuy Chu, Quang-Vinh Vo, and Hong-Quang Nguyen, "Intelligent Control of Ackermann Autonomous Vehicles via Neural Network-Enhanced Fuzzy-PID: A Simscape Multibody-Based Simulation Study," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 4, pp. 343-352, 2026. doi: 10.18178/ijmerr.15.4.343-352

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).