Research map / Learning theory and algorithms / Combinatorial and search optimization
Infinite-Horizon Differentiable Model Predictive Control
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase the prediction horizon (ablation) | Infinite-Horizon Differentiable MPC · Mass-spring-damper imitation learning | convergence speed: converge faster as the prediction horizon is increased | Helped |
| increase the prediction horizon (ablation) | Infinite-Horizon Differentiable MPC · Mass-spring-damper imitation learning | model correctness: N=2 | Mixed |
| increase the prediction horizon to N=6 (ablation) | Infinite-Horizon Differentiable MPC · Mass-spring-damper closed-loop trajectories | trajectory agreement: N = 6 | Helped |
| increase the prediction horizon to at least N=4 (ablation) | Infinite-Horizon Differentiable MPC · Mass-spring-damper closed-loop trajectories | state constraint satisfaction: N ≥ 4 | Helped |
| increase the prediction horizon to N=15 or N=20 (ablation) | Infinite-Horizon Differentiable MPC · Vehicle platooning imitation learning | imitation loss: N ∈ {15,20} | Helped |
| use prediction horizon N=20 (ablation) | Infinite-Horizon Differentiable MPC · Vehicle platooning closed-loop simulation | prediction accuracy: N=20 | Helped |
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