Data driven control for a class of nonlinear systems with output saturation
This paper considers the problem of data driven control (DDC) for a class of non-affine
nonlinear systems with output saturation. A time varying linear data model for such nonlinear …
nonlinear systems with output saturation. A time varying linear data model for such nonlinear …
[图书][B] Iterative learning control for multi-agent systems coordination
A timely guide using iterative learning control (ILC) as a solution for multi-agent systems
(MAS) challenges, showcasing recent advances and industrially relevant applications …
(MAS) challenges, showcasing recent advances and industrially relevant applications …
Robust tracking of nonrepetitive learning control systems with iteration-dependent references
D Meng, J Zhang - IEEE Transactions on Systems, Man, and …, 2018 - ieeexplore.ieee.org
Typically, iterative learning control (ILC) is applied based on a core hypothesis that the strict
repetitiveness of control environment, task, and model should be satisfied by the controlled …
repetitiveness of control environment, task, and model should be satisfied by the controlled …
Neural network-based iterative learning control of a piezo-driven nanopositioning stage
The piezo-driven nanopositioning stage (PNS) is a key device to provide fast and precise
motions for applications such as micromanipulation, microfabrication, and microscopy …
motions for applications such as micromanipulation, microfabrication, and microscopy …
AI-MOLE: Autonomous Iterative Motion Learning for unknown nonlinear dynamics with extensive experimental validation
Abstract This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method
that enables systems with unknown, nonlinear dynamics to autonomously learn to solve …
that enables systems with unknown, nonlinear dynamics to autonomously learn to solve …
Tracking algorithms for multiagent systems
D Meng, Y Jia, J Du, F Yu - IEEE Transactions on neural …, 2013 - ieeexplore.ieee.org
This paper is devoted to the consensus tracking issue on multiagent systems. Instead of
enabling the networked agents to reach an agreement asymptotically as the time tends to …
enabling the networked agents to reach an agreement asymptotically as the time tends to …
Model free adaptive iterative learning control for a class of nonlinear systems with randomly varying iteration lengths
This paper proposes a novel model free adaptive iterative learning control scheme for a
class of unknown nonlinear systems with randomly varying iteration lengths. By applying the …
class of unknown nonlinear systems with randomly varying iteration lengths. By applying the …
Iterative learning control of iteration-varying systems via robust update laws with experimental implementation
Iterative learning control (ILC) is an efficient way of improving the tracking performance of
repetitive systems. While ILC can offer significant improvement to the transient response of …
repetitive systems. While ILC can offer significant improvement to the transient response of …
Iterative learning tracking for multisensor systems: A weighted optimization approach
Multisensor systems are widely applied to realize the comprehensive monitoring and control
as they feature multiple individual sensors/outputs. In such systems, different sensors can …
as they feature multiple individual sensors/outputs. In such systems, different sensors can …
High-order internal model-based iterative learning control for 2-D linear FMMI systems with iteration-varying trajectory tracking
K Wan, X Li - IEEE Transactions on Systems, Man, and …, 2019 - ieeexplore.ieee.org
This paper is concerned with iterative learning control (ILC) algorithms for two-dimensional
(2-D) linear discrete systems described by the first Fornasini-Marchesini model (FMMI) with …
(2-D) linear discrete systems described by the first Fornasini-Marchesini model (FMMI) with …