In order to guarantee stability, known results for MPC without additionalterminal costs or endpoint constraints often require rather large predictionhorizons. Still, stable behavior of closed loop solutions can often be observedeven for shorter horizons. Here, we make use of the recent observation thatstability can be guaranteed for smaller prediction horizons via Lyapunovarguments if more than only the first control is implemented. Since such aprocedure may be harmful in terms of robustness, we derive conditions whichallow to increase the rate at which state measurements are used for feedbackwhile maintaining stability and desired performance specifications. Our maincontribution consists in developing two algorithms based on the deducedconditions and a corresponding stability theorem which ensures asymptoticstability for the MPC closed loop for significantly shorter predictionhorizons.
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