Two-dimensional iterative learning robust asynchronous switching predictive control for multiphase batch processes with time-varying delays

H Li, S Wang, H Shi, C Su, P Li - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This study formulated an iterative learning-based predictive control strategy for
asynchronous switching of multiphase batch processes with complex characteristics in the …

Adaptive learning control for nonlinear systems with randomly varying iteration lengths

D Shen, JX Xu - IEEE transactions on neural networks and …, 2018 - ieeexplore.ieee.org
This paper proposes adaptive iterative learning control (ILC) schemes for continuous-time
parametric nonlinear systems with iteration lengths that randomly vary. As opposed to the …

Disturbance rejection based on iterative learning control with extended state observer for a four-degree-of-freedom hybrid magnetic bearing system

X Sun, Z Jin, L Chen, Z Yang - Mechanical Systems and Signal Processing, 2021 - Elsevier
Iterative learning control (ILC) is an iterative control strategy which calculates a new input
according to the error in previous cycles. It is widely used in industries with repetitive …

Multipoint iterative learning model predictive control

J Lu, Z Cao, F Gao - IEEE Transactions on Industrial Electronics, 2018 - ieeexplore.ieee.org
Iterative learning model predictive control (ILMPC), endowed with the merits of iterative
learning control and model predictive control, has excellent abilities of disturbance rejection …

Iterative learning control for time-varying systems subject to variable pass lengths: Application to robot manipulators

J Shi, J Xu, J Sun, Y Yang - IEEE Transactions on Industrial …, 2019 - ieeexplore.ieee.org
In this article, the iterative learning control (ILC) problem is investigated for a class of
stochastic time-varying systems with variable pass lengths. The randomness of the pass …

Multi-scale data-driven engineering for biosynthetic titer improvement

Z Cao, J Yu, W Wang, H Lu, X Xia, H Xu, X Yang… - Current Opinion in …, 2020 - Elsevier
Industrial biosynthesis is a very complex process which depends on a range of different
factors, from intracellular genes and metabolites, to extracellular culturing conditions and …

A 2D-FM model-based robust iterative learning model predictive control for batch processes

L Wang, J Yu, P Li, H Li, R Zhang - ISA transactions, 2021 - Elsevier
The work deals with composite iterative learning model predictive control (CILMPC) for
uncertain batch processes via a two dimensional Fornasini–Marchesini (2D-FM) model. A …

Robust model predictive iterative learning control for iteration-varying-reference batch processes

X Liu, L Ma, X Kong, KY Lee - IEEE Transactions on Systems …, 2019 - ieeexplore.ieee.org
Model predictive iterative learning control (MPILC) is a popular approach to control batch
systems with repetitive nature, as it is capable of tracking the plant reference trajectory with …

Data-driven soft sensing for batch processes using neural network-based deep quality-relevant representation learning

Q Jiang, Z Wang, S Yan, Z Cao - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
Soft sensors provide a means to reliably estimate unmeasurable variables, thereby playing
a prevalent role in formulating closed-loop control in batch processes. In soft sensor …

A hybrid 2D fault-tolerant controller design for multi-phase batch processes with time delay

Y Shen, L Wang, J Yu, R Zhang, F Gao - Journal of Process Control, 2018 - Elsevier
Considering inevitable time delays, actuator faults and other issues in practical industrial
batch processes, a two dimensional (2D) robust iterative learning fault-tolerant controller …