Performance Prediction of NMPC Algorithms with Incomplete Optimization

Abstract

Nonlinear model predictive control (NMPC) in real-time applications leads to a trade-off between computation time and solution accuracy, resulting in uncertainty about the control quality. This paper faces this issue by monitoring and predicting the cost of the MPC in every optimization step. A heteroscedastic Gaussian process regression method is developed that can be used to predict the number of optimization steps necessary for a sufficient cost decrease. The method is demonstrated on an example.