Iterative learning control for flexible manipulator using fourier basis function

2015 ◽  
Vol 12 (6) ◽  
pp. 639-647 ◽  
Author(s):  
Li Zhang ◽  
Shan Liu
Keyword(s):  
Basis Function ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Flexible Manipulator ◽  
Fourier Basis

scholarly journals Performance Of Hybrid Learning Control With Input Shaping For Input Tracking And Vibration Suppression Of A Flexible Manipulator

2012 ◽  
Author(s):  
M. Z. Md. Zain ◽  
M. O. Tokhi ◽  
Z. Mohamed
Keyword(s):  
Iterative Learning Control ◽  
Vibration Suppression ◽  
Hybrid Learning ◽  
Learning Control ◽  
Iterative Learning ◽  
Body Motion ◽  
Flexible Manipulator ◽  
Input Shaping ◽  
Control Input ◽  
Control Scheme

Objektif kertas kerja ini ialah untuk mengkaji keberkesanan gabungan pengawal pembelajaran berulang cerdik dan teknik pembentuk masukan bagi penjejakan masukan dan pengurangan getaran pada hujung suatu pengolah fleksibel. Model dinamik sistem tersebut diterbitkan menggunakan kaedah unsur terhingga. Pada permulaan, pengawal kadaran–kebezaan (PD) menggunakan sudut dan halaju hub direka bentuk untuk kawalan pergerakan badan tegar sistem. Kemudian, pengawal pembelajaran berulang dengan algoritma genetik dan pengawal suap hadapan berasaskan teknik pembentuk masukan ditambahkan untuk kawalan getaran sistem. Keputusan simulasi dalam domain masa dan frekuensi diberikan. Keberkesanan pengawal yang direka bentuk ini dikaji berasaskan penjejakan masukan dan kadar pengurangan getaran sistem. Keberkesanan pengawal ini untuk sistem pengolah fleksibel berbagai beban juga dikaji. Kata kunci: Pengolah fleksibel, algoritma genetik, kawalan cerdik, kawalan pembelajaran berulang, pembentukan masukan The objective of the work reported in this paper is to investigate the performance of an intelligent hybrid iterative learning control scheme with input shaping for input tracking and end–point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using finite element method. Initially, a collocated proportional–derivative (PD) controller utilizing hub–angle and hub–velocity feedback is developed for control of rigid–body motion of the system. This is then extended to incorporate iterative learning control with genetic algorithm (GA) to optimize the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in time and frequency domains. The performance of hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied. Key words: Flexible manipulator, genetic algorithms, intelligent control, iterative learning control, input shaping

Hybrid Learning Control With Input Shaping for Input Tracking and Vibration Suppression of a Flexible Manipulator

Volume 1 ◽  
2004 ◽  
Author(s):  
M. Z. Md Zain ◽  
M. O. Tokhi ◽  
Z. Mohamed
Keyword(s):  
Iterative Learning Control ◽  
Vibration Suppression ◽  
Hybrid Learning ◽  
Learning Control ◽  
Iterative Learning ◽  
Body Motion ◽  
Flexible Manipulator ◽  
Input Shaping ◽  
Control Schemes ◽  
Frequency Domains

The objective of the work reported in this paper is to investigate the development of hybrid iterative learning control with input shaping for input tracking and end-point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectives of the control schemes in handling various payloads are also studied.

Iterative Learning Control Based on Radial Basis Function Network for Exoskeleton Arm

2011 ◽  
Vol 415-417 ◽  
pp. 116-122 ◽  
Author(s):  
Jie Liu ◽  
Yu Wang ◽  
He Ting Tong ◽  
Ray P.S. Han
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Iterative Learning Control ◽  
Rbf Neural Network ◽  
Radial Basis Function Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Radial Basis

In this paper, we propose iterative learning control (ILC) scheme for exoskeleton arm driven by pneumatic artificial muscles (PAM), with special and unknown parameters, performing repetitive tasks. This desired control input of ILC was estimated by radial basis function (RBF) neural network incorporated experience database. An ILC controller, which uses the position of the joint where an angular sensor is used as the input of the ILC controller, is developed and tested on exoskeleton arm under well controlled conditions. RBF neural network was proposed to obtain the initial value of ILC. The experiment result on the experimental platform show that the algorithm is successful also in the application of exoskeleton arm.

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