Implementation of model predictive control in simulations and real-world vehicle tests
Ke, Youwei
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https://hdl.handle.net/2142/129614
Description
Title
Implementation of model predictive control in simulations and real-world vehicle tests
Author(s)
Ke, Youwei
Issue Date
2025-05-06
Director of Research (if dissertation) or Advisor (if thesis)
Hovakimyan, Naira
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
MPC
Autonomous vehicles
Obstacle avoidance
Occupancy grid
Abstract
This study presents a real-time motion planning and control framework for autonomous ground vehicles, with a primary focus on deploying a Model Predictive Control strategy directly onto a real-world vehicle platform. The objective is to enable safe, adaptive obstacle avoidance in dynamically changing environments by solving constrained nonlinear optimal control problems in real time. The framework is based on non-linear model predictive control. It continuously optimizes vehicle motion by anticipating future states while respecting dynamic constraints and environmental interactions. Control inputs are optimized over a finite prediction horizon, explicitly considering the vehicle’s kinematics, dynamics, actuator limits, and obstacle constraints. This predictive strategy ensures smooth, stable, and timely maneuvers during obstacle avoidance, promoting both safety and ride comfort.
To address the computational demands of real-time implementation, two solver backends are developed. The first is a Sequential Quadratic Programming Real-Time Iteration solver implemented in ACADOS. The second is a Differential Dynamic Programming solver implemented using CasADi. The Sequential Quadratic Programming Real-Time Iteration method offers fast convergence and is suitable for structured optimization. In contrast, the Differential Dynamic Programming approach provides greater flexibility for handling system nonlinearities and complex obstacle configurations. This dual-solver structure allows for flexible adaptation depending on the specific requirements of the environment and computational resources available during deployment.
During simulation studies, environmental information such as obstacle distributions is modeled using occupancy grid maps. This setup allows evaluation of the effectiveness of the control system under structured and repeatable conditions. Occupancy grids provide a probabilistic representation of the environment. This representation enables efficient testing of the control framework in scenarios with dense and uncertain obstacle fields. However, in real-world experiments, the framework relies directly on live perception data collected from onboard sensors. These sensors include LiDAR, cameras, radar, and GNSS/INS systems. Real-time sensor fusion transforms this data into actionable obstacle representations. It allows the vehicle to detect, interpret, and respond dynamically to changes in the surrounding environment without relying on pre-generated static maps.
The effectiveness of the proposed system is validated through both simulation and real-world tests using an autonomous ground vehicle. Simulation experiments provide a structured evaluation of control smoothness, obstacle avoidance success, and system robustness under different obstacle densities. Real-world tests further show that the framework can achieve reliable obstacle avoidance and stable trajectory tracking. It also performs well in complex, unstructured, and changing environments. These results confirm that a Nonlinear Model Predictive Control-based obstacle avoidance system can be applied effectively to real-time autonomous driving tasks.
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