Horizon Adaptation for Nonlinear Model Predictive Controllers with guaranteed Degree of Suboptimality
For control engineers using model predictive control, this provides theoretical guarantees on suboptimality when adapting the horizon, though it is an incremental extension of existing theory.
The paper proposes adaptation strategies for modifying the optimization horizon in nonlinear model predictive control to guarantee a lower bound on suboptimality, with proven shortening and prolongation strategies and extensions of stability results.
We propose adaptation strategies to modify the standard constrained model predictive controller scheme in order to guarantee a certain lower bound on the degree of suboptimality. Within this analysis, the length of the optimization horizon is the parameter we wish to adapt. We develop and prove several shortening and prolongation strategies which also allow for an effective implementation. Moreover, extensions of stability results and suboptimality estimates to model predictive controllers with varying optimization horizon are shown.