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Found 19 papers, 1 papers with code
Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints
Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years.
Data Analysis, Statistics and Probability Statistical Mechanics
A Computationally Efficient Model for Pedestrian Motion Prediction
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.
Systems and Control
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?
For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy.
Reinforcement Learning for Mixed-Integer Problems Based on MPC
Model Predictive Control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning.
Safe Trajectory Tracking in Uncertain Environments
In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility.
Constrained Controller and Observer Design by Inverse Optimality
When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active.
Learning for MPC with Stability & Safety Guarantees
The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature.
Stability-Constrained Markov Decision Processes Using MPC
This observation will entail that the MPC-based policy with stability requirements will produce the optimal policy for the discounted MDP if it is stable, and the best stabilizing policy otherwise.
Economic MPC of Markov Decision Processes: Dissipativity in Undiscounted Infinite-Horizon Optimal Control
Economic Model Predictive Control (MPC) dissipativity theory is central to discussing the stability of policies resulting from minimizing economic stage costs.
A New Dissipativity Condition for Asymptotic Stability of Discounted Economic MPC
Economic Model Predictive Control has recently gained popularity due to its ability to directly optimize a given performance criterion, while enforcing constraint satisfaction for nonlinear systems.
Model Predictive Control with Infeasible Reference Trajectories
Model Predictive Control (MPC) formulations are typically built on the requirement that a feasible reference trajectory is available.
Data-driven synthesis of Robust Invariant Sets and Controllers
This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC).
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections
In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders.
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control
This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an inexact model.
Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System
We perform high-performance Monte Carlo simulations in C enabled by a new thread-safe NMPC implementation in combination with an existing high-performance Monte Carlo simulation toolbox in C. We express the NMPC regulator as an optimal control problem (OCP), which we solve with the new thread-safe sequential quadratic programming software NLPSQP.
Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching
The PI tuning offers a wide range of tuning possibilities that is then inherited by the MPC design.
Computation of safe disturbance sets using implicit RPI sets
Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches as closely as possible the output constraint set, while being included in it.
Learning disturbance models for offset-free reference tracking
This work presents a nonlinear MPC framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch.
Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections
In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs).
