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Found 24 papers, 1 papers with code
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
The existing methods of computing probabilistic reachable sets normally assume that the uncertainties are independent of the state.
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach
In the environment, we propose a Q-learning-based PeDP to maximize fast-EVCS' revenue.
Once upon a time step: A closed-loop approach to robust MPC design
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization.
Deep active learning for nonlinear system identification
This simulated dataset can be used in a static deep active learning acquisition scheme referred to as global explorations.
Learning-based MPC from Big Data Using Reinforcement Learning
The state-of-the-art learning methods use RL to improve the performance of parameterized MPC schemes.
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.
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC
In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as exogenous signals with inexact bounds), where the Linear Time Invariant (LTI) models (vertices) captured by combinations of the scheduling parameters becomes wrong.
Bridging the gap between QP-based and MPC-based RL
We propose simple tools to promote structures in the QP, pushing it to resemble a linear MPC scheme.
Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level
For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms.
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory
This paper discusses the functional stability of closed-loop Markov Chains under optimal policies resulting from a discounted optimality criterion, forming Markov Decision Processes (MDPs).
Quasi-Newton Iteration in Deterministic Policy Gradient
We show that the approximate Hessian converges to the exact Hessian at the optimal policy, and allows for a superlinear convergence in the learning, provided that the policy parametrization is rich.
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.
Backstepping-based Integral Sliding Mode Control with Time Delay Estimation for Autonomous Underwater Vehicles
The aim of this paper is to propose a high performance control approach for trajectory tracking of Autonomous Underwater Vehicles (AUVs).
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems.
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles
In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning (RL) method for Autonomous Surface Vehicles (ASVs).
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning
In the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop stability relates to the existence of a storage function satisfying a dissipation inequality.
Approximate Robust NMPC using Reinforcement Learning
We present a Reinforcement Learning-based Robust Nonlinear Model Predictive Control (RL-RNMPC) framework for controlling nonlinear systems in the presence of disturbances and uncertainties.
Bias Correction in Deterministic Policy Gradient Using Robust MPC
In this paper, we discuss the deterministic policy gradient using the Actor-Critic methods based on the linear compatible advantage function approximator, where the input spaces are continuous.
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage
In this paper, we are interested in optimal control problems with purely economic costs, which often yield optimal policies having a (nearly) bang-bang structure.
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics
This paper proposes an observer-based framework for solving Partially Observable Markov Decision Processes (POMDPs) when an accurate model is not available.
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensuring safe operation.
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
In control applications there is often a compromise that needs to be made with regards to the complexity and performance of the controller and the computational resources that are available.
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 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.
