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Found 15 papers, 0 papers with code
Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems
This is achieved by first determining a set of inputs that minimize the probability of constraint violation.
Combining Belief Function Theory and Stochastic Model Predictive Control for Multi-Modal Uncertainty in Autonomous Driving
Therefore, the measure of reliability of the estimation provided by Belief Function Theory is used in the design of collision-avoidance safety constraints, in particular to increase safety when the intention of traffic participants is not clear.
Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty
We present a stochastic output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances and probabilistic chance constraints.
Optimal Control for Indoor Vertical Farms Based on Crop Growth
Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security.
Offline Uncertainty Sampling in Data-driven Stochastic MPC
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise.
Safe Stochastic Model Predictive Control
In this work, we propose a safety algorithm that is compatible with any stochastic Model Predictive Control method for linear systems with additive uncertainty and polytopic constraints.
MITI: SLAM Benchmark for Laparoscopic Surgery
We propose a new benchmark for evaluating stereoscopic visual-inertial computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial Odometry) for minimally invasive surgical (MIS) interventions in the abdomen.
Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery
We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements.
Data-driven tube-based stochastic predictive control
A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data.
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller.
Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control.
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version
Automated vehicles require efficient and safe planning to maneuver in uncertain environments.
Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments
Then, the probabilistic grid is transformed into a binary grid of admissible and inadmissible cells by applying a threshold, representing a risk parameter.
Minimization of Constraint Violation Probability in Model Predictive Control
We propose a novel Model Predictive Control scheme that yields a solution with minimal constraint violation probability for a norm constraint in an environment with uncertainty.
Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity
We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions.
