Key result
An update strategy based on re-optimizations on shrinking horizons for model predictive control is computationally less expensive and allows for rigorously quantifiable robust performance estimates.
Population
Perturbed nonlinear discrete time systems
Comparison
Update strategy based on re-optimizations on… vs Full horizon re-optimization
Design
Other
Authors
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Enables efficient robust MPC for nonlinear systems; leaves open applied validation before broader adoption.
This paper proposes a computationally efficient model predictive control update strategy for perturbed nonlinear discrete time systems.
Grüne et al. (2015) studied this question. An update strategy based on re-optimizations on shrinking horizons for model predictive control is computationally less expensive and allows for rigorously quantifiable robust performance estimates.
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