MPC Algorithms with Stability and Performance Guarantees

Details MPC Algorithms with Stability and Performance Guarantees MPC Algorithms with Stability and Performance Guarantees

2011-09-28

arxiv.org/abs/1109.6153v1

Mathematics

Optimization and Control

20 pages, 7 figures

Scientific paper

A typical bottleneck of model predictive control algorithms is the computational burden in order to compute the receding horizon feedback law which is predominantly determined by the length of the prediction horizon. Based on a relaxed Lyapunov inequality we present techniques which allow us to show stability and suboptimality estimates for a reduced prediction horizon. In particular, the known structural properties of suboptimality estimates based on a controllability condition are used to cut the gap between theoretic stability results and numerical and practical observations.

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