Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Hossein Nejatbakhsh"'
Publikováno v:
Mechanics of Advanced Composite Structures, Vol 11, Iss 2, Pp 351-362 (2024)
The aeroelastic stability of the tail is significantly challenged by flutter instability. Skin and spars strongly affect flutter speed due to their torsional and bending stiffness, respectively. C-section spars are primarily utilized in composite str
Externí odkaz:
https://doaj.org/article/4c9ac2c1cfdb4774a2ce7925b8faa6c3
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
Externí odkaz:
http://arxiv.org/abs/2206.05089
This paper presents a model-free approximation for the Hessian of the performance of deterministic policies to use in the context of Reinforcement Learning based on Quasi-Newton steps in the policy parameters. We show that the approximate Hessian con
Externí odkaz:
http://arxiv.org/abs/2203.13854
The aim of this paper is to propose a high performance control approach for trajectory tracking of Autonomous Underwater Vehicles (AUVs). However, the controller performance can be affected by the unknown perturbations including model uncertainties a
Externí odkaz:
http://arxiv.org/abs/2111.10179
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. An approximate Robust Nonlinear Model Predictive Control (RN
Externí odkaz:
http://arxiv.org/abs/2104.02743
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. When the policy is restricted by hard constraints, the e
Externí odkaz:
http://arxiv.org/abs/2104.02413
Autor:
Kordabad, Arash Bahari, Esfahani, Hossein Nejatbakhsh, Lekkas, Anastasios M., Gros, Sébastien
Publikováno v:
2021 American Control Conference (ACC), 1985-1990
In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and target (set-point) tra
Externí odkaz:
http://arxiv.org/abs/2103.11949
This paper proposes an observer-based framework for solving Partially Observable Markov Decision Processes (POMDPs) when an accurate model is not available. We first propose to use a Moving Horizon Estimation-Model Predictive Control (MHE-MPC) scheme
Externí odkaz:
http://arxiv.org/abs/2103.11871
Publikováno v:
In Ocean Engineering 1 November 2019 191
Publikováno v:
Polish Maritime Research, Vol 26, Iss 3, Pp 163-171 (2019)
This paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynami
Externí odkaz:
https://doaj.org/article/1ab2e898d14549ca8072471387c9d8bb