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Date of Award
ABSTRACTMODEL PREDICTIVE CONTROL FOR ON-ROAD AUTONOMOUS DRIVING by RUSHANTH SAI GANESH THATI JEEVANANDAM May 2022 Advisor: Dr. Azad Ghaffari Major: Mechanical Engineering Degree: Master of Science There has been much interest in self-driving vehicles due to their potential to reduce road accidents and transform people's lives. Increasing embedded computing capabilities coupled with the lowering costs of advanced sensing technologies have enabled highly automated driving to be commercialized. Autonomous vehicles continue to face many challenges, one of which is ensuring reliable operation for the technology to be widely adopted and deployed. Research in this thesis investigates the challenges involved in designing control strategies for on-road self-driving vehicles performing under different driving scenarios. We unify elements of vehicle dynamics modeling, real-time optimization, and control design under uncertainty to enable the reliable operation of self-driving vehicles. Applications to lane-keeping assistance, autonomous left turns, and autonomous overtaking on highways provide evidence of the effectiveness of the proposed framework based on model predictive control. Our experimental vehicle dynamic model helps us build predictive models of the vehicle based on driving scenarios and determine the level of uncertainty. We present a range of control designs depending on the application. We ensure desirable vehicle performance by calculating admissible reference trajectories and embedding state and input constraints into the control design. Simulations on dynamic vehicle models validate the effectiveness of the proposed control methodology in handling a variety of driving scenarios.
Thati Jeevanandam, Rushanth Sai Ganesh, "Model Predictive Control For On-Road Autonomous Driving" (2022). Wayne State University Theses. 869.