Max-Min-Plus-Scaling Neural Networks to Approximate Continuous Piecewise Affine Model Predictive Control Laws

This thesis extensively examines the influential factors affecting the performance of approximations of Model Predictive Control (MPC) control laws using neural networks. MPC is a control strategy that solves an optimization problem at each timestep. This problem can be computationally complex and could be too slow to compute for online control....

Integrating human drivers' behavioural rules into microscopic traffic simulation

Autonomous vehicles (AVs) can solve a lot of problems related to traffic safety, comfort and congestion. While the sensors used by these vehicles are getting cheaper, more accurate, and software is improving, the first completely automated vehicle does not exist yet. When AVs start gradually driving on the roads, there will be a mixed traffic...

Optimal Sub-References for Setpoint Tracking: A Multi-level MPC Approach

We propose a novel method to improve the convergence performance of model predictive control (MPC) for setpoint tracking, by introducing sub-references within a multilevel MPC structure. In some cases, MPC is implemented with a short prediction horizon due to limited on-line computation capacity, which could lead to deteriorated dynamic...