Model Predictive Control Algorithm Based on Signal Temporal Logic Specifications with Disturbances Guaranteeing Feasibility

In this research, we deal with model predictive control problem with disturbances based on Signal Temporal Logic specifications. In this paper, we theoretically analyze the feasibility of model predictive control problems and derive sufficient conditions for recursive feasibility. The proposed algorithm has the advantage that recursive feasibility can be determined in advance (by initial conditions). Finally, we show the effectiveness of the proposed method by confirming that the theoretical and numerical results agree with each other under generic vehicle driving scenarios, using self-driving cars.

Authors

Institutions

Publication Details

Published
2024-10-07
DOI
https://doi.org/10.1109/vtc2024-fall63153.2024.10757775
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model Predictive Control Algorithm Based on Signal Temporal Logic Specifications with Disturbances Guaranteeing Feasibility

Toru Namerikawa, Akira Kuchiki
Advanced Control Systems Optimization
article

Model Predictive Control Algorithm Based on Signal Temporal Logic Specifications with Disturbances Guaranteeing Feasibility

Toru Namerikawa, Akira Kuchiki
article en

Abstract

In this research, we deal with model predictive control problem with disturbances based on Signal Temporal Logic specifications. In this paper, we theoretically analyze the feasibility of model predictive control problems and derive sufficient conditions for recursive feasibility. The proposed algorithm has the advantage that recursive feasibility can be determined in advance (by initial conditions). Finally, we show the effectiveness of the proposed method by confirming that the theoretical and numerical results agree with each other under generic vehicle driving scenarios, using self-driving cars.

Keio University (JP)
Openalex Percentile: Top 69%
Advanced Control Systems Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.