Sliding modes after the first decade of the 21st century
This book is a collection of the Plenary and Semiplenary talks in the joint 11th IEEE
Workshop on Variable Structure Systems (VSS2010) and the principal meeting for the …
Workshop on Variable Structure Systems (VSS2010) and the principal meeting for the …
Model-free predictive current control of synchronous reluctance motors based on a recurrent neural network
Recently, model-based predictive current control (MB-PCC) has been presented as a good
alternative to classical control algorithms in terms of simplicity and performance reliability …
alternative to classical control algorithms in terms of simplicity and performance reliability …
Neural high order sliding mode control for doubly fed induction generator based wind turbines
Wind energy has many advantages because it does not pollute and is an inexhaustible
source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is …
source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is …
Adaptive tracking control of state constraint systems based on differential neural networks: A barrier Lyapunov function approach
RQ Fuentes-Aguilar, I Chairez - IEEE transactions on neural …, 2020 - ieeexplore.ieee.org
The aim of this article is to investigate the trajectory tracking problem of systems with
uncertain models and state restrictions using differential neural networks (DNNs). The …
uncertain models and state restrictions using differential neural networks (DNNs). The …
[图书][B] Discrete-time inverse optimal control for nonlinear systems
EN Sanchez, F Ornelas-Tellez - 2013 - books.google.com
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse
optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear …
optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear …
Identification and adaptive PID Control of a hexacopter UAV based on neural networks
In this paper, a novel adaptive PID controller for trajectory‐tracking tasks is proposed. It is
implemented in discrete time over a hexacopter, and it takes into consideration the …
implemented in discrete time over a hexacopter, and it takes into consideration the …
Neural networks modeling and control: applications for unknown nonlinear delayed systems in discrete time
Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed
Systems in Discrete Time focuses on modeling and control of discrete-time unknown …
Systems in Discrete Time focuses on modeling and control of discrete-time unknown …
Neural control of an induction motor with regenerative braking as electric vehicle architecture
E Quintero-Manríquez, EN Sanchez… - … Applications of Artificial …, 2021 - Elsevier
This paper presents the synthesis of an induction motor neural controller and a regenerative
braking controller for an electric vehicle architecture, based on two energy system, a Main …
braking controller for an electric vehicle architecture, based on two energy system, a Main …
Real‐time neural sliding mode field oriented control for a DFIG‐based wind turbine under balanced and unbalanced grid conditions
L Djilali, EN Sanchez, M Belkheiri - IET Renewable Power …, 2019 - Wiley Online Library
This study proposes a real‐time sliding mode field oriented control for a doubly‐fed
induction generator (DFIG)‐based wind turbine prototype connected to the grid. The …
induction generator (DFIG)‐based wind turbine prototype connected to the grid. The …
Real-time neural inverse optimal control for a wind generator
R Ruiz-Cruz, EN Sanchez… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
This paper presents a discrete-time inverse optimal control scheme using a neural network
for a doubly fed induction generator (DFIG). The DFIG generation scheme has a …
for a doubly fed induction generator (DFIG). The DFIG generation scheme has a …