On the learning control of a robot manipulator

1991 ◽  
Vol 4 (1) ◽  
pp. 43-53 ◽  
Author(s):  
D. M. Dawson ◽  
Z. Qu ◽  
J. F. Dorsey ◽  
F. L. Lewis
Keyword(s):  
Robot Manipulator ◽  
Learning Control

Experiment Verification and Stability Analysis of Iterative Learning Control for Shape Memory Alloy Wire

2019 ◽  
Vol 31 (4) ◽  
pp. 583-593
Author(s):  
Hitoshi Kino ◽  
Naofumi Mori ◽  
Shota Moribe ◽  
Kazuyuki Tsuda ◽  
Kenji Tahara ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Iterative Learning Control ◽  
Robot Manipulator ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Methods ◽  
Experiment Verification ◽  
And Control

To achieve the control of a small-sized robot manipulator, we focus on an actuator using a shape memory alloy (SMA). By providing an adjusted voltage, an SMA wire can itself generate heat, contract, and control its length. However, a strong hysteresis is generally known to be present in a given heat and deformation volume. Most of the control methods developed thus far have applied detailed modeling and model-based control. However, there are many cases in which it is difficult to determine the parameter settings required for modeling. By contrast, iterative learning control is a method that does not require detailed information on the dynamics and realizes the desired motion through iterative trials. Despite pioneering studies on the iterative learning control of SMA, convergence has yet to be proven in detail. This paper therefore describes a stability analysis of an iterative learning control to mathematically prove convergence at the desired length. This paper also details an experimental verification of the effect of convergence depending on the variation in gain.

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

2016 ◽  
Vol 40 (1) ◽  
pp. 49-60 ◽  
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.

Learning control of a parallel-link direct-drive robot manipulator

1989 ◽  
Vol 5 (2) ◽  
pp. 127-134 ◽  
Author(s):  
Yasuo Konishi ◽  
Tojiro Aoyama ◽  
Ichiro Inasaki
Keyword(s):  
Robot Manipulator ◽  
Learning Control ◽  
Direct Drive ◽  
Parallel Link
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