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.

A Two-Stage Model Based Iterative Learning Control Scheme for a Class of MIMO Mismatched Linear Systems

2012 ◽  
Author(s):  
Wenjie Chen ◽  
Masayoshi Tomizuka
Keyword(s):  
Linear Systems ◽  
Iterative Learning Control ◽  
Ad Hoc ◽  
Learning Control ◽  
Iterative Learning ◽  
Feedback Signal ◽  
Control Input ◽  
Model Based ◽  
Control Scheme ◽  
Indirect Drive

This paper discusses the tracking control problem for a class of multi-input-multi-output (MIMO) mismatched linear systems, where there are disturbances in different channels from the control input and the real-time feedback signal is not the output of interest. This mismatch makes it difficult to achieve high tracking performance for the interested output. To address this problem, two model based iterative learning control (ILC) algorithms, namely reference ILC and torque ILC, are designed for different injection locations in the closed loop system. An ad hoc hybrid scheme is proposed to make transitions between the two ILC stages for them to work properly at the same time. The proposed scheme is validated through the experimental study on a single-joint indirect drive system.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

Reference adjustment for a high-acceleration and high-precision platform via A-type of iterative learning control

2007 ◽  
Vol 221 (5) ◽  
pp. 781-789 ◽  
Author(s):  
J. H. Wu ◽  
H Ding
Keyword(s):  
High Precision ◽  
Iterative Learning Control ◽  
Control Structure ◽  
Learning Control ◽  
Iterative Learning ◽  
High Acceleration ◽  
Control Scheme ◽  
Point Motion ◽  
Point To Point ◽  
P Type

This paper studies the repetitive motion control of a high-acceleration and high-precision platform driven by linear motors. The control scheme comprises an anticipatory iterative learning control (A-ILC) component and a cascaded control structure including an inner-loop velocity PI controller and an outer-loop position P controller. During the motion process, the cascaded controller remains invariant while the A-ILC adjusts the reference command cycle by cycle to achieve better performance. Experiments are carried out to validate the proposed control structure. The results confirm that the proposed control scheme can improve the system performance significantly in both low-speed trajectory tracking motions and fast point-to-point motion. In the experiments, P-type and D-type ILCs are also utilized to adjust the reference command. Compared with the A type, P-type ILC leads to larger tracking error bounds and D-type ILC lacks a fast convergence rate for low-speed motions, while for fast point-to-point motion these two types of ILC are unable to work well.

Adaptive robust iterative learning control with application to a Delta robot

2020 ◽  
pp. 095965182093853
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Derivative Type ◽  
Delta Robot ◽  
Proportional Derivative Controller

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

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