Model reference fuzzy learning force control for robotized sewing

2011 ◽  
Author(s):  
Dimitra Triantafyllou ◽  
Panagiotis N. Koustoumpardis ◽  
Nikos A. Aspragathos
Keyword(s):  
Force Control ◽  
Model Reference ◽  
Fuzzy Learning

Model Reference Adaptive Force Control in Milling

1989 ◽  
Vol 111 (1) ◽  
pp. 13-21 ◽  
Author(s):  
L. K. Lauderbaugh ◽  
A. G. Ulsoy
Keyword(s):  
Force Control ◽  
Adaptive Controller ◽  
Signal Frequency ◽  
Unmodeled Dynamics ◽  
Model Reference ◽  
Computational Speed ◽  
Overall Performance ◽  
Model Reference Adaptive ◽  
Digital Simulations ◽  
Adaptive Force Control

This paper describes the design and implementation of a Model Reference Adaptive Controller (MRAC) for force control in milling. First, previous work in this area is discussed. Results from previous work on the performance of fixed gain process controllers is summarized. The design of an MRAC for force control in milling is described, including a discussion of the implementation issues of noise and computational speed. The adaptive controller was found to perform more satisfactorily than fixed gain controllers, but is difficult to implement and tune, primarily because of the unmodeled dynamics or measurement noise resulting from runout on the milling cutter. In this problem there was sufficient separation between the noise and the signal frequency that the noise could be filtered. However, the addition of the filter added additional dynamics to the system which reduced the overall performance from that expected from digital simulations.

scholarly journals Adaptive Control Design and Stability Analysis of Robotic Manipulators

Actuators ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 89 ◽  
Author(s):  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Stability Analysis ◽  
Motion Control ◽  
Force Control ◽  
Control Design ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Model Reference Adaptive

In this paper, the author presents the adaptive control design and stability analysis of robotic manipulators based on two main approaches, i.e., Lyapunov stability theory and hyperstability theory. For the Lyapunov approach, the author presents the adaptive control of a 2-DOF (degrees of freedom) robotic manipulator. Furthermore, the adaptive control technique and Lyapunov theory are subsequently applied to the end-effector motion control and force control, as in most cases, one only considers the motion control (e.g., position control, trajectory tracking). To make the robot interact with humans or the environment, force control must be considered as well to achieve a safe working environment. For the hyperstability approach, a control system is developed through integrating a PID (proportional–integral–derivative) control system and a model reference adaptive control (MRAC) system, and also the convergent behavior and characteristics under the situation of the PID system, model reference adaptive control system, and PID+MRAC control system are compared.

Impaired anticipation of force control following lateralised brain damage in size-weight illusion

2008 ◽  
Vol 35 (S 01) ◽  
Author(s):  
Y Li ◽  
J Randerath ◽  
G Goldenberg ◽  
J Hermsdörfer
Keyword(s):  
Brain Damage ◽  
Force Control ◽  
Weight Illusion
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