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.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

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.

scholarly journals PDα-type iterative learning control with initial state learning for fractional-order systems

2021 ◽  
Vol 39 (2) ◽  
pp. 400-406
Author(s):  
Fen Liu ◽  
Kejun Zhang
Keyword(s):  
Fractional Order ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Sufficient Conditions ◽  
Learning Control ◽  
Young Inequality ◽  
Iterative Learning ◽  
Initial State ◽  
Arbitrary Initial State ◽  
State Learning

In order to eliminate the influence of the arbitrary initial state on the systems, open-loop and open-close-loop PDα-type fractional-order iterative learning control (FOILC) algorithms with initial state learning are proposed for a class of fractional-order linear continuous-time systems with an arbitrary initial state. In the sense of Lebesgue-p norm, the sufficient conditions for the convergence of PDα-type algorithms are disturbed in the iteration domain by taking advantage of the generalized Young inequality of convolution integral. The results demonstrate that under these novel algorithms, the convergences of the tracking error are can be guaranteed. Numerical simulations support the effectiveness and correctness of the proposed algorithms.

The CPG Gait Generate Method of the Quadruped Robot Based on Iterative Learning Control Algorithm

2013 ◽  
Vol 677 ◽  
pp. 296-303 ◽  
Author(s):  
Cheng Wang ◽  
Jun Yao Gao ◽  
Xing Guang Duan ◽  
Yi Liu ◽  
Xin Li ◽  
...  
Keyword(s):  
Control System ◽  
Control Theory ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Quadruped Robot ◽  
Structure Design ◽  
Iterative Learning ◽  
Hydraulic Power ◽  
Prototype Design

Based on the quadruped robot, this paper mainly studies the two directions of the content. The first part mainly introduces the mechanical structure design and the construction of the control system of the quadruped robot, completes the prototype design of the quadruped robot based on hydraulic power system. The second part studies the CPG gait generate method of the quadruped robot based on iterative learning control algorithm. From the principle of bionics, firstly, we use the CPG principle to generate gait, and then use the iterative learning control theory to make the control more optimization.

scholarly journals ILC for Non-Linear Hyperbolic Partial Difference Systems

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3076
Author(s):  
Meryem Hamidaoui ◽  
Cheng Shao
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Sufficient Conditions ◽  
Learning Control ◽  
Iterative Learning ◽  
Monotone Convergence ◽  
Initial State ◽  
Partial Difference ◽  
Gronwall’S Inequality ◽  
Non Linear

This paper discusses the iterative learning control problem for a class of non-linear partial difference system hyperbolic types. The proposed algorithm is the PD-type iterative learning control algorithm with initial state learning. Initially, we introduced the hyperbolic system and the control law used. Subsequently, we presented some dilemmas. Then, sufficient conditions for monotone convergence of the tracking error are established under the convenient assumption. Furthermore, we give a detailed convergence analysis based on previously given lemmas and the discrete Gronwall’s inequality for the system. Finally, we illustrate the effectiveness of the method using a numerical example.

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