Echo state network-based backstep** adaptive iterative learning control for strict-feedback systems: An error-tracking approach
Q Chen, H Shi, M Sun - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
In this article, an echo state network (ESN)-based backstep** adaptive iterative learning
control scheme is proposed for nonlinear strict-feedback systems performing the same …
control scheme is proposed for nonlinear strict-feedback systems performing the same …
State of the art of repetitive control in power electronics and drive applications
M Tang, M di Benedetto, S Bifaretti… - IEEE Open Journal …, 2021 - ieeexplore.ieee.org
Power electronic systems present a non-linear behavior mainly due to their switching nature.
This is often combined with their interaction with non-linear systems, such as other switching …
This is often combined with their interaction with non-linear systems, such as other switching …
Microstep** using a disturbance observer and a variable structure controller for permanent-magnet stepper motors
W Kim, D Shin, CC Chung - IEEE Transactions on Industrial …, 2012 - ieeexplore.ieee.org
The application of a disturbance observer (DOB) and a variable structure controller (VSC) to
current control of microstep** for permanent-magnet stepper motors (PMSMs) is studied …
current control of microstep** for permanent-magnet stepper motors (PMSMs) is studied …
Inner loop design for PMLSM drives with thrust ripple compensation and high-performance current control
M Wang, R Yang, C Zhang, J Cao… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
To realize the precise thrust force control in permanent magnet linear synchronous motor
(PMLSM) drives, this paper studies a novel design method of the high-performance current …
(PMLSM) drives, this paper studies a novel design method of the high-performance current …
Iterative learning control with variable sampling frequency for current control of grid-connected converters in aircraft power systems
This paper investigates the feasibility of an iterative learning control (ILC) with variable
sampling frequency for current control of power converters in frequency-wild power systems …
sampling frequency for current control of power converters in frequency-wild power systems …
Nonlinear Control for a Nonlinear System With Bounded Varying Parameters: Application to PM Stepper Motors
Y Lee, D Shin, W Kim, CC Chung - IEEE/ASME Transactions on …, 2017 - ieeexplore.ieee.org
We have designed a nonlinear H 2 controller for permanent magnet stepper motors. The
proposed method consists of a new torque modulation scheme, a passive nonlinear …
proposed method consists of a new torque modulation scheme, a passive nonlinear …
Experimental heart rate regulation in cycle-ergometer exercises
M Paradiso, S Pietrosanti, S Scalzi… - IEEE Transactions …, 2012 - ieeexplore.ieee.org
The heart rate can be effectively used as a measure of the exercise intensity during long
duration cycle-ergometer exercises: precisely controlling the heart rate (HR) becomes …
duration cycle-ergometer exercises: precisely controlling the heart rate (HR) becomes …
Linear repetitive learning controls for robotic manipulators by Padé approximants
The aim of this brief is to present the use of [m, m]-Padé approximants in the implementation
of repetitive learning controls for the asymptotic joint position tracking of robotic manipulators …
of repetitive learning controls for the asymptotic joint position tracking of robotic manipulators …
Learning control in spatial coordinates for the path-following of autonomous vehicles
We prove the existence of a P-type (proportional-type) space-learning control, which, on the
basis of a kinematic third order nonlinear model of an autonomous nonholonomic vehicle …
basis of a kinematic third order nonlinear model of an autonomous nonholonomic vehicle …
Learning control for nonlinear systems in output feedback form
The class of single-input, single-output, minimum phase, nonlinear, time-invariant systems
with unknown output-dependent nonlinearities, unknown parameters and known relative …
with unknown output-dependent nonlinearities, unknown parameters and known relative …