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....