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Found 24 papers, 5 papers with code
A Comparison Benchmark for Distributed Hybrid MPC Control Methods: Distributed Vehicle Platooning
We propose distributed control of a platoon of autonomous vehicles as a comparison benchmark problem.
State-action control barrier functions: Imposing safety on learning-based control with low online computational costs
Learning-based control with safety guarantees usually requires real-time safety certification and modifications of possibly unsafe learning-based policies.
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator
Existing work on RL has demonstrated the use of model predictive control (MPC) as a function approximator for the policy and value functions.
Model Predictive Control
Multi-agent Reinforcement Learning
+3
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of highway ramp metering control that embeds Reinforcement Learning techniques within the Model Predictive Control framework.
Model-based Reinforcement Learning
Model Predictive Control
+1
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration
In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems within the Linear Quadratic Gaussian (LQG) framework.
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics should often be considered to avoid overly conservative decisions.
A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
Moreover, a unified view on different network models is lacking.
Efficient MPC for Emergency Evasive Maneuvers, Part II: Comparative Assessment for Hybrid Control
In this paper, we construct hybrid formulations of the nonlinear MPC problem for tracking control during emergency evasive maneuvers and assess their computational efficiency in terms of accuracy and solution time.
Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem
In this paper, we introduce a hybridization approach for efficient approximation of nonlinear vehicle dynamics and non-convex constraints using a hybrid systems modeling framework.
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees
Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline design and online implementation.
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control
In this work, we aim to analyze how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller.
Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression.
Approximate Dynamic Programming for Constrained Linear Systems: A Piecewise Quadratic Approximation Approach
A novel convex and piecewise quadratic neural network with a local-global architecture is proposed to provide an accurate approximation of the value function, which is used as the cost-to-go function in the online dynamic programming problem.
Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching
To capture the effect of the parameters of the switching strategies on the LS estimation error, finite-sample error bounds are developed in this work.
Error-free approximation of explicit linear MPC through lattice piecewise affine expression
The performance of the proposed approximation strategy is tested through two simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.
Constrained Sampling from a Kernel Density Estimator to Generate Scenarios for the Assessment of Automated Vehicles
In this paper, we propose a method to sample from a pdf estimated using KDE, such that the samples satisfy a linear equality constraint.
A real-time distributed post-disaster restoration planning strategy for distribution networks
After disasters, distribution networks have to be restored by repair, reconfiguration, and power dispatch.
Scenario-based Nonlinear Model Predictive Control for Building Heating Systems
In this article, we combine a stochastic scenario-based MPC (SBMPC) controller together with a nonlinear Modelica model that is able to provide a richer building description and to capture the dynamics of the building more accurately than linear models.
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms.
Real-World Scenario Mining for the Assessment of Automated Vehicles
One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios.
Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e. g.~operational planning, power purchases, reserve activation, etc.
Forecasting day-ahead electricity prices in Europe: the importance of considering market integration
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance.
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.
Residential Demand Response Applications Using Batch Reinforcement Learning
We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.
