Reducing the Prediction Horizon in NMPC: An Algorithm Based Approach

Details Reducing the Prediction Horizon in NMPC: An Algorithm Based Approach Reducing the Prediction Horizon in NMPC: An Algorithm Based Approach

2011-05-17

arxiv.org/abs/1105.3267v1

Proceedings of the 18th IFAC World Congress, 2011, 7969-7974

Mathematics

Optimization and Control

6 pages, 3 figures

Scientific paper

In order to guarantee stability, known results for MPC without additional terminal costs or endpoint constraints often require rather large prediction horizons. Still, stable behavior of closed loop solutions can often be observed even for shorter horizons. Here, we make use of the recent observation that stability can be guaranteed for smaller prediction horizons via Lyapunov arguments if more than only the first control is implemented. Since such a procedure may be harmful in terms of robustness, we derive conditions which allow to increase the rate at which state measurements are used for feedback while maintaining stability and desired performance specifications. Our main contribution consists in developing two algorithms based on the deduced conditions and a corresponding stability theorem which ensures asymptotic stability for the MPC closed loop for significantly shorter prediction horizons.

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