A KL-divergence trust-region formulation for sampling-based MPC replaces heuristic hyperparameter adaptation with Lagrangian-optimal updates and improves convergence when combined with deterministic LCD sampling.
Dirac mixture approxi- mation of multivariate Gaussian densities
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.SY 2verdicts
UNVERDICTED 2representative citing papers
dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.
citing papers explorer
-
Sampling-based Model Predictive Control Using Trust Regions
A KL-divergence trust-region formulation for sampling-based MPC replaces heuristic hyperparameter adaptation with Lagrangian-optimal updates and improves convergence when combined with deterministic LCD sampling.
-
Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.