Real-Time Experimental Evaluation and Analysis of PID and MPC Controllers Using HIL Setup for Robust Steering System of Autonomous Vehicles
S. Gokul Krishnan, P. Suresh Kumar, Nadheem Nassar Matara, Yong Wang
Abstract
The development of autonomous vehicles has recently received substantial impetus, fueled by researchers and industry personnel. The need for powerful steering control in autonomous vehicles is critical for assuring the vehicles’ safety and reliability. Robust steering control allows for precise and accurate maneuvering, allowing the vehicle to traverse complicated road conditions. Comparative research on the certification of a robust steering system for autonomous vehicles is presented in this paper. Traditional controllers (PD and PID) are compared with a modern Model Predictive Control (MPC) controller that uses a multi-turn potentiometer and incremental encoder for position feedback. The controllers are designed in MATLAB Simulink and deployed for real-time testing on a Speedgoat performance real-time target Hardware-in-the-Loop (HIL) machine. The study focuses on evaluating the steering system’s real-time performance in terms of accuracy and robustness. The novelty is thiswork is carried out in a real experimental modified electric vehicle and presents real-time results obtained using the HIL machine. The research covers a thorough examination of the experimental hardware configuration, system identification, controller design, and data-gathering technologies. A significant contribution of this research is the use of the HIL machine for real-time performance testing of different controllers with different velocities and sample times, specifically in a speed breaker scenario. To analyze each controller’s response, real-time response data is logged at a high sampling rate of 0.1 milliseconds. The research contributes to the advancement of driverless vehicles by providing insights into the optimal performance of steering systems. It also emphasizes the importance of real-time testing of different controllers’ robust performance in ensuring human safety in driverless cars.