Iterative learning control of a single-link flexible manipulator based on an identified adaptive NARX model

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

Download Full-text

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

Volume 1 ◽

10.1115/esda2004-58239 ◽

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.

Download Full-text

Sliding mode based fractional-order iterative learning control for a nonlinear robot manipulator with bounded disturbance

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331216659130 ◽

2016 ◽

Vol 40 (1) ◽

pp. 49-60 ◽

Cited By ~ 6

Author(s):

Iman Ghasemi ◽

Abolfazl Ranjbar Noei ◽

Jalil Sadati

Keyword(s):

Fractional Order ◽

Iterative Learning Control ◽

Sliding Mode ◽

Robot Manipulator ◽

Learning Control ◽

Iterative Learning ◽

Control Law ◽

Sliding Mode Controller ◽

Text Type ◽

Single Link

In this paper a new type of sliding mode based fractional-order iterative learning control (ILC) is proposed for nonlinear systems in the presence of uncertainties. For the first time, a sliding mode controller is combined with fractional-order ILC. This sliding mode based [Formula: see text] and [Formula: see text]-type ILC is applied on a nonlinear robot manipulator. Convergence of the proposed method is investigated when the stability is also proved. In this method, the control signal at any iteration is generated in two parts. The first section comes from the sliding mode controller while the second part is output of the fractional-order ILC. The latter signal is assessed using its previous amount and the sliding mode error signal. The achieved control law is capable of controlling nonlinear iterative processes, perturbed by bounded disturbances with high accuracy. The same frequent disturbance is eliminated by the iterative learning part, while the effect of nonrepetitive uncertainty is improved by the sliding mode part. The sliding mode based [Formula: see text]-type ILC (as an adaptive control law) is proposed to control a single-link arm robot. The controller is then improved to sliding mode based [Formula: see text]-type ILC. The effectiveness of the proposed method is again investigated on a single-link robot manipulator through a simulation approach. It is shown that the controller for [Formula: see text] provides performance by means of faster response together with more accuracy with respect to a conventional ILC.

Download Full-text