Simulation of an iterative learning control system for fed-batch cell culture processes

1992 ◽  
Vol 10 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Pengcheng Fu ◽  
John P. Barford
Keyword(s):  
Cell Culture ◽  
Control System ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Fed Batch ◽  
Batch Cell

Online Wall Thickness Control for Blow Molded Part Using Hybrid Optimization Method and Fuzzy Iterative Learning Control Algorithm

2008 ◽  
Author(s):  
Geng-Qun Huang ◽  
Han-Xiong Huang
Keyword(s):  
Control System ◽  
Wall Thickness ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Thickness Distribution ◽  
Learning Control ◽  
Optimization Method ◽  
Iterative Learning ◽  
Thickness Control ◽  
Molded Part

An online wall thickness control strategy for the extrusion blow molded part was proposed in this work. A simulation-based optimization method combining with finite element, artificial neural network, and genetic algorithm was used to determine the initial die gap profile for a part with required thickness distribution. A multi-channel ultrasonic thickness measurement system was built up to get the in-mold wall thickness of the blow molded part. Then, a feedback closed-loop control system based on fuzzy iterative learning control algorithm was designed and implemented to control the wall thickness of blow molded part. The results showed that the online wall thickness control system developed in this work can automatically achieve a proper die gap profile and get the satisfied part thickness distribution.

Analysis of iterative learning control for an oscillating control system with two delays

2017 ◽  
Vol 40 (6) ◽  
pp. 1757-1765 ◽  
Author(s):  
Chengbin Liang ◽  
JinRong Wang
Keyword(s):  
Control System ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Reference Trajectory ◽  
Output State ◽  
Control Functions ◽  
Input Control ◽  
Two Delays

In order to track the desired reference trajectory from an oscillating control system with two delays in a finite time interval, we design iterative learning control updating laws to generate a sequence of input control functions such that the error between the output and the desired reference trajectories tends to zero via a suitable norm in the sense of uniform convergence. Here, we adopt a delayed matrix function to characterize the output state, which can be easily solved in the simulation. As a result, convergence analysis results are given. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.

Optimal Design of a Hybrid-Driven Servo Press and Experimental Verification

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Pei-Lum Tso
Keyword(s):  
Control System ◽  
Optimal Design ◽  
Energy Saving ◽  
Experimental Verification ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Servo Press ◽  
Ac Motor ◽  
Control Theories

This paper focuses on a hybrid-driven servo press, which uses not only a servomotor but also a regular ac motor with a flywheel. A PC-based control system is developed based on both feedback and iterative learning control theories on a prototype. The stamping performance, improved forming ability, and energy saving merits have been verified by the experiment. The results show that the advantages of the hybrid-driven servo press have been validated, such as its energy saving features, flexible punch speeds, and adjustable strokes, for any kind of stamping operations.

An iterative learning control algorithm based on time varying pilot factor

2019 ◽  
Vol 25 (8) ◽  
pp. 1484-1491 ◽  
Author(s):  
Jing Huang ◽  
Zhenxiang Xu ◽  
Guoxiu Li ◽  
Cheng Qiu ◽  
Haitao Huang
Keyword(s):  
Control System ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Sufficient Conditions ◽  
Learning Control ◽  
Critical Issue ◽  
Iterative Learning ◽  
Time Varying ◽  
Initial State ◽  
Hydraulic Servo

Owing to the control system being repetitive and nonlinear, a time-varying pilot factor control algorithm based on iterative learning control is proposed. The convergence of the TPF-ILC control algorithm is mathematically proven and the sufficient conditions are given. Thereafter, the initial state issue of iterative learning is explored, which is the critical issue of iterative learning control. The convergence of the system’s control error and the initial state of every single period have been mathematically proved by using continuous and repetitive properties of the system, even if the initial states of every single iterative learning period are not strictly the same. At the end of this paper, the TPF-ILC algorithm is applied in a hydraulic servo control system, and experimental results indicate the effectiveness and practicability of the TPF-ILC algorithm.

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