MPC-based vehicle trajectory tracking using machine learning for parameter optimization and fault detection

Abstract

This thesis explores advancements in trajectory tracking control and fault detection within automated vehicle systems, focusing on two main areas: developing a learning-based model predictive control algorithm to enhance tracking accuracy and evaluating various neural networks as fault detection systems for trajectory tracking controllers. Both parts are assessed in a high-fidelity simulation environment. The first part presents two adaptive model predictive controllers that use vehicle information, trajectory data, and tracking information to adapt the vehicle model within the model predictive control system, compensating for lost tracking accuracy due to model mismatches. One approach employs a trajectorydynamic lookup table, while the more advanced approach uses Gaussian process regression with clustering. A thorough simulation study on real-world racetracks with varying dynamics demonstrates that the advanced approach effectively manages condition changes, significantly improves tracking performance, handles unknown trajectories with similar improvements, and memorizes adapted behavior through clustering. The second part evaluates the effectiveness of four types of neural networks as fault detection systems. These networks detect changes in the vehicle, environmental shifts, or discrepancies between the applied vehicle model and the real vehicle. Trained a priori through supervised learning, the networks use tracking information, controller outputs, and vehicle data. The evaluation distinguishes between known and unknown fault conditions. The results suggest that neural networks are generally suitable for fault detection systems. Differences in effectiveness among the network types are minor for known fault conditions but more significant for unknown conditions. Integrating adaptive model predictive control and neural network-based fault detection systems shows promise for developing robust and fault-tolerant control systems, enhancing accuracy and maintaining operational integrity in dynamic environments for trajectory tracking.