Nonlinear model predictive control with enhanced actuator model for multi-rotor aerial vehicles with generic designs
In this paper, we propose, discuss, and validate an online Nonlinear Model Predictive
Control (NMPC) method for multi-rotor aerial systems with arbitrarily positioned and oriented …
Control (NMPC) method for multi-rotor aerial systems with arbitrarily positioned and oriented …
Near-optimal rapid MPC using neural networks: A primal-dual policy learning framework
X Zhang, M Bujarbaruah… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this article, we propose a novel framework for approximating the MPC policy for linear
parameter-varying systems using supervised learning. Our learning scheme guarantees …
parameter-varying systems using supervised learning. Our learning scheme guarantees …
A direct visual servoing‐based framework for the 2016 IROS Autonomous Drone Racing Challenge
This paper presents a framework for navigating in obstacle‐dense environments as posed in
the 2016 International Conference on Intelligent Robots and Systems (IROS) Autonomous …
the 2016 International Conference on Intelligent Robots and Systems (IROS) Autonomous …
Development of model predictive controller for a Tail-Sitter VTOL UAV in hover flight
This paper presents a model predictive controller (MPC) for position control of a vertical take-
off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A 'cross' …
off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A 'cross' …
Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles
Abstract In this paper, Levenberg–Marquardt inspired sliding mode control theory based
adaptation laws are proposed to train an intelligent fuzzy neural network controller for a …
adaptation laws are proposed to train an intelligent fuzzy neural network controller for a …
Sympocnet: Solving optimal control problems with applications to high-dimensional multiagent path planning problems
Solving high-dimensional optimal control problems in real-time is an important but
challenging problem, with applications to multiagent path planning problems, which have …
challenging problem, with applications to multiagent path planning problems, which have …
Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton–Jacobi PDEs
Solving high-dimensional optimal control problems and corresponding Hamilton–Jacobi
PDEs are important but challenging problems in control engineering. In this paper, we …
PDEs are important but challenging problems in control engineering. In this paper, we …
Deep transfer learning for approximate model predictive control
Transfer learning is a machine learning technique that takes a pre-trained model that has
already been trained on a related task, and adapts it for use on a new, related task. This is …
already been trained on a related task, and adapts it for use on a new, related task. This is …
Extended state observer-based robust backstepping sliding mode control for a small-size helicopter
M He, J He - IEEE Access, 2018 - ieeexplore.ieee.org
This paper addresses the design and application controller for a small-size unmanned aerial
vehicle. A new robust sliding mode controller (SMC) is proposed to improve the performance …
vehicle. A new robust sliding mode controller (SMC) is proposed to improve the performance …
Design and real-time implementation of a wireless autopilot using multivariable predictive generalized minimum variance control in the state-space
The contribution of this work is the numerical simulation and the experimental assessment of
a network distributed control system using an unmanned aerial vehicle and a remote master …
a network distributed control system using an unmanned aerial vehicle and a remote master …