Search Results for author:
Found 39 papers, 16 papers with code
Learning Hierarchical Control Systems for Autonomous Systems with Energy Constraints
This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints.
Clustering Heuristics for Robust Energy Capacitated Vehicle Routing Problem (ECVRP)
The paper presents an approach to solving the Robust Energy Capacitated Vehicle Routing Problem (RECVRP), focusing on electric vehicles and their limited battery capacity.
Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs).
Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios.
Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification
Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.
Increasing Electric Vehicles Utilization in Transit Fleets using Learning, Predictions, Optimization, and Automation
These predictions are then used by a Predictive Block Assignment module to maximize the BEB fleet utilization.
Robust Output-Lifted Learning Model Predictive Control
We propose an iterative approach for designing Robust Learning Model Predictive Control (LMPC) policies for a class of nonlinear systems with additive, unmodelled dynamics.
Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates
We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control.
Output Feedback Stochastic MPC with Hard Input Constraints
We present an output feedback stochastic model predictive controller (SMPC) for constrained linear time-invariant systems.
Collaborative learning model predictive control for repetitive tasks
This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost; a coordinator, which assigns the tasks to the agents; and the cloud, which stores data to facilitate the agents' learning.
Stochastic MPC with Realization-Adaptive Constraint Tightening
A sample-based strategy is used to compute sets of disturbance sequences necessary for robustifying the state chance constraints.
Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions
The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
Overtaking Maneuvers on a Nonplanar Racetrack
We leverage game theory and a new vehicle modeling approach to compute overtaking maneuvers for racecars on a nonplanar surface.
Vehicle Models and Optimal Control on a Nonplanar Surface
We present a 10 DoF dynamic vehicle model for model-based control on nonplanar road surfaces.
ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer
The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper.
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria
Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios.
Stochastic MPC with Multi-modal Predictions for Traffic Intersections
The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios.
Monocular Camera Localization for Automated Vehicles Using Image Retrieval
The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs.
Compact Cooperative Adaptive Cruise Control for Energy Saving: Air Drag Modelling and Simulation
This paper studies the value of communicated motion predictions in the longitudinal control of connected automated vehicles (CAVs).
Accelerating Quadratic Optimization with Reinforcement Learning
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Data-Driven Strategies for Hierarchical Predictive Control in Unknown Environments
In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories.
Models and Predictive Control for Nonplanar Vehicle Navigation
We present a simplified model of a vehicle driving on a nonplanar road.
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Learning How to Solve Bubble Ball
"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball.
Learning to Play Cup-and-Ball with Noisy Camera Observations
In this paper, we present a learning model based control strategy for the cup-and-ball game, where a Universal Robots UR5e manipulator arm learns to catch a ball in one of the cups on a Kendama.
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Learning to Satisfy Unknown Constraints in Iterative MPC
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space.
Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints.
Learning Robustness with Bounded Failure: An Iterative MPC Approach
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task.
Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks
At each iteration of the control task the closed-loop state, input and cost are stored and used in the controller design.
Adaptive MPC under Time Varying Uncertainty: Robust and Stochastic
We consider a linear system, in presence of bounded time varying additive uncertainty.
Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks
In contrast to most existing approaches, we not only learn the control policy, but also a "certificate policy", that allows us to estimate the sub-optimality of the learned control policy online, during execution-time.
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Task Decomposition for Iterative Learning Model Predictive Control
A task decomposition method for iterative learning model predictive control is presented.
Distributed Model Predictive Control for Heterogeneous V ehicle Platoons Under Unidirectional Topologies
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point.
Learning Model Predictive Control for Iterative Tasks: A Computationally Efficient Approach for Linear System
The control scheme is reference-free and is able to improve its performance by learning from previous iterations.
Autonomous Racing using Learning Model Predictive Control
A novel learning Model Predictive Control technique is applied to the autonomous racing problem.
Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework
The controller is reference-free and is able to improve its performance by learning from previous iterations.
