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 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

scholarly journals Iterative learning control with sampled-data feedback for robot manipulators

2014 ◽  
Vol 24 (3) ◽  
pp. 299-319 ◽  
Author(s):  
Kamen Delchev ◽  
George Boiadjiev ◽  
Haruhisa Kawasaki ◽  
Tetsuya Mouri
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Sampled Data ◽  
Model Based ◽  
Data Feedback

Abstract This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

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