The use of Feed-Forward Identification Scheme in Industrial Robots Adaptive Control

1987 ◽  
pp. 488-494
Author(s):  
D. Matko ◽  
B. Nemec
Keyword(s):  
Adaptive Control ◽  
Industrial Robots ◽  
Feed Forward ◽  
Identification Scheme

Roboterbasierte spanende Bearbeitung*/Robot-Based Milling Operation. Machine Learning Algorithm for a model-based feed-forward torque control

2019 ◽  
Vol 109 (05) ◽  
pp. 352-357
Author(s):  
C. Brecher ◽  
L. Gründel ◽  
L. Lienenlüke ◽  
S. Storms
Keyword(s):  
Machine Learning ◽  
Position Control ◽  
Learning Algorithm ◽  
Milling Process ◽  
Industrial Robots ◽  
Torque Control ◽  
Feed Forward ◽  
Control Loops ◽  
Model Based ◽  
Machine Learning Approach

Die Lageregelung von konventionellen Industrierobotern ist nicht auf den dynamischen Fräsprozess ausgelegt. Eine Möglichkeit, das Verhalten der Regelkreise zu optimieren, ist eine modellbasierte Momentenvorsteuerung, welche in dieser Arbeit aufgrund vieler Vorteile durch einen Machine-Learning-Ansatz erweitert wird. Hierzu wird die Umsetzung in Matlab und die simulative Evaluation erläutert, die im Anschluss das Potenzial dieses Konzeptes bestätigt.   The position control of conventional industrial robots is not designed for the dynamic milling process. One possibility to optimize the behavior of the control loops is a model-based feed-forward torque control which is supported by a machine learning approach due to many advantages. The implementation in Matlab and the simulative evaluation are explained, which subsequently confirms the potential of this concept.

Object Manipulation Using Compliant Fingerpads: Modeling and Control

1993 ◽  
Vol 115 (4) ◽  
pp. 638-648 ◽  
Author(s):  
A. M. Annaswamy ◽  
D. Seto
Keyword(s):  
Adaptive Control ◽  
Feedback Linearization ◽  
Industrial Robots ◽  
Dynamic Interactions ◽  
Modeling And Control ◽  
Grasp Stability ◽  
Grasping And Manipulation ◽  
End Effectors ◽  
Geometric Techniques ◽  
The Stability

Current industrial robots are often required to perform tasks requiring mechanical interactions with their environment. For tasks that require grasping and manipulation of unknown objects, it is crucial for the robot end-effector to be compliant to increase grasp stability and manipulability. The dynamic interactions that occur between such compliant end-effectors and deformable objects that are being manipulated can be described by a class of nonlinear systems. In this paper, we determine algorithms for grasping and manipulation of these objects by using adaptive feedback techniques. Methods for control and adaptive control of the underlying nonlinear system are described. It is shown that although standard geometric techniques for exact feedback linearization techniques are inadequate, yet globally stable adaptive control algorithms can be determined by making use of the stability characteristics of the underlying nonlinear dynamics.

Design of an adaptive control law using trigonometric functions for robot manipulators

Robotica ◽  
2005 ◽  
Vol 23 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Recep Burkan
Keyword(s):  
Adaptive Control ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Uncertain System ◽  
Control Law ◽  
Trigonometric Functions ◽  
Feed Forward ◽  
Inertia Parameters ◽  
The Stability ◽  
Adaptive Control Law

In this study, a new approach of adaptive control law for controlling robot manipulators using the Lyapunov based theory is derived, thus the stability of an uncertain system is guaranteed. The control law includes a PD feed forward part and a full dynamics feed forward compensation part with the unknown manipulator and payload parameters. The novelty of the obtained result is that an adaptive control algorithm is developed using trigonometric functions depending on manipulator kinematics, inertia parameters and tracking error, and both system parameters and adaptation gain matrix are updated in time.

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