Overview of predictive control technology for permanent magnet synchronous motor systems
J Peng, M Yao - Applied Sciences, 2023 - mdpi.com
Permanent magnet synchronous motors (PMSMs) are commonly used in the automation
industry. With the speedy development of digital system processors, predictive control as a …
industry. With the speedy development of digital system processors, predictive control as a …
Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives
This paper addresses control performance monitoring (CPM) and degradation recovering in
automatic control systems. It begins with a re-visit of CPM techniques and a summary of the …
automatic control systems. It begins with a re-visit of CPM techniques and a summary of the …
Online learning‐based predictive control of crystallization processes under batch‐to‐batch parametric drift
This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and
presents the methodology and implementation of a real‐time machine learning modeling …
presents the methodology and implementation of a real‐time machine learning modeling …
Disturbances attenuation of permanent magnet synchronous motor drives using cascaded predictive-integral-resonant controllers
Z Zhou, C Xia, Y Yan, Z Wang… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The performance of a standard model predictive controller (MPC) is directly related to its
predictive model. If there are unmodeled periodic disturbances in the actual system, MPC …
predictive model. If there are unmodeled periodic disturbances in the actual system, MPC …
Multipoint iterative learning model predictive control
Iterative learning model predictive control (ILMPC), endowed with the merits of iterative
learning control and model predictive control, has excellent abilities of disturbance rejection …
learning control and model predictive control, has excellent abilities of disturbance rejection …
Iterative learning model predictive control based on iterative data-driven modeling
L Ma, X Liu, X Kong, KY Lee - IEEE transactions on neural …, 2020 - ieeexplore.ieee.org
Iterative learning model predictive control (ILMPC) has been recognized as an effective
approach to realize high-precision tracking for batch processes with repetitive nature …
approach to realize high-precision tracking for batch processes with repetitive nature …
Reduced-order model based dynamic tracking for soft manipulators: Data-driven LPV modeling, control design and experimental results
We propose a generic nonlinear reduced-order tracking control method for elastic soft
robots. To this end, a new linear parameter varying (LPV) control framework is developed …
robots. To this end, a new linear parameter varying (LPV) control framework is developed …
Iterative learning hybrid robust predictive fault-tolerant control for nonlinear batch processes with partial actuator faults
H Li, S Wang, H Shi, C Su, P Li - Journal of Process Control, 2023 - Elsevier
For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an
iterative learning robust predictive fault-tolerant control approach is developed. Based on …
iterative learning robust predictive fault-tolerant control approach is developed. Based on …
A 2D-FM model-based robust iterative learning model predictive control for batch processes
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 …
uncertain batch processes via a two dimensional Fornasini–Marchesini (2D-FM) model. A …
Terminal constrained robust hybrid iterative learning model predictive control for complex time-delayed batch processes
This work mainly addresses terminal constrained robust hybrid iterative learning model
predictive control against time delay and uncertainties in a class of complex batch processes …
predictive control against time delay and uncertainties in a class of complex batch processes …