Application of Learning Control to Active Damping of Forced Vibration for Periodically Time Variant Systems

1990 ◽  
Vol 112 (4) ◽  
pp. 489-496 ◽  
Author(s):  
A. Hac´ ◽  
M. Tomizuka
Keyword(s):  
Learning Control ◽  
Forced Vibrations ◽  
Control Algorithms ◽  
Convergence Properties ◽  
Periodic Disturbances ◽  
Control Rules ◽  
Stiffness Properties ◽  
Feedforward Controller ◽  
Simulation Results ◽  
Inertia Forces

This paper deals with discrete time learning controllers for systems with periodically varying parameters. Several control rules are proposed which use a basic principle of learning control that is to utilize the information from the most recent cycle to improve the system performance in the next cycle. Convergence properties of proposed learning control algorithms are examined. These algorithms can easily be implemented without additional measurements and without modification of existing feedback/feedforward controller. In a numerical example learning controllers are applied to eliminate the forced vibrations of a magnetically suspended rotor with nonsymmetric stiffness properties. The vibrations result from unbalanced inertia forces. Simulation results show that the learning controller is effective in absorbing periodic disturbances.

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

Learning Control of Robot Manipulators

1993 ◽  
Vol 115 (2B) ◽  
pp. 402-411 ◽  
Author(s):  
Roberto Horowitz
Keyword(s):  
Adaptive Learning ◽  
Robot Manipulators ◽  
Learning Control ◽  
Integral Transforms ◽  
Stability And Convergence ◽  
Control Algorithms ◽  
Convergence Properties ◽  
Motor Dexterity ◽  
The Stability ◽  
Learning Schemes

Learning control encompasses a class of control algorithms for programmable machines such as robots which attain, through an iterative process, the motor dexterity that enables the machine to execute complex tasks. In this paper we discuss the use of function identification and adaptive control algorithms in learning controllers for robot manipulators. In particular, we discuss the similarities and differences between betterment learning schemes, repetitive controllers and adaptive learning schemes based on integral transforms. The stability and convergence properties of adaptive learning algorithms based on integral transforms are highlighted and experimental results illustrating some of these properties are presented.

Research on Dead-End Tunneling Ventilation Control System

2012 ◽  
Vol 516-517 ◽  
pp. 1188-1191
Author(s):  
Shu Fang Wang ◽  
Zhi Yong Yang ◽  
Ming Hai Li
Keyword(s):  
Concentration Distribution ◽  
Experimental System ◽  
Ventilation Mode ◽  
Smoke Emission ◽  
Control Rules ◽  
Dead End ◽  
Co Concentration ◽  
Simulation Results ◽  
Speed Adjustment ◽  
Normal Ventilation

On the basis of analysis of ventilation requirement and CO concentration distribution character in dead-end tunneling, this paper designed the ventilation equipment layout. Furthermore, a control strategy which includes normal ventilation mode and gun smoke discharging mode is established. In view of expert experience and numerical simulation results dead-end tunneling, fuzzy control is adopted to deal with this problem. Control rules principle is described in detail. Applying direct torque speed adjustment mode, an experimental system is designed and implemented. Partial experimental results show that gun smoke emission process is fast in order to increase efficiency, while normal ventilation mode is adjustable air flow for energy conservation.

scholarly journals Mechatronic Device for Locomotor Training

2016 ◽  
Vol 10 (4) ◽  
pp. 310-315 ◽  
Author(s):  
Sławomir Duda ◽  
Damian Gąsiorek ◽  
Grzegorz Gembalczyk ◽  
Sławomir Kciuk ◽  
Arkadiusz Mężyk
Keyword(s):  
Experimental Data ◽  
Numerical Simulation ◽  
Measurement Data ◽  
Healthy Person ◽  
Control Algorithms ◽  
Design Team ◽  
Locomotor Training ◽  
Interdisciplinary Design ◽  
Machine Control ◽  
Simulation Results

Abstract This paper presents a novel mechatronic device to support a gait reeducation process. The conceptual works were done by the interdisciplinary design team. This collaboration allowed to perform a device that would connect the current findings in the fields of biomechanics and mechatronics. In the first part of the article shown a construction of the device which is based on the structure of an overhead travelling crane. The rest of the article contains the issues related to machine control system. In the prototype, the control of drive system is conducted by means of two RT-DAC4/PCI real time cards connected with a signal conditioning interface. Authors present the developed control algorithms and optimization process of the controller settings values. The summary contains a comparison of some numerical simulation results and experimental data from the sensors mounted on the device. The measurement data were obtained during the gait of a healthy person.

ILC-Based Internal Model Control of Superheater Steam Temperature

2013 ◽  
Vol 732-733 ◽  
pp. 864-869
Author(s):  
Wei Lu ◽  
Ming Chun Wang
Keyword(s):  
Iterative Learning Control ◽  
Internal Model ◽  
Handling Time ◽  
Learning Control ◽  
Internal Model Control ◽  
Iterative Learning ◽  
Steam Temperature ◽  
Periodic Disturbances ◽  
Modeling Uncertainties ◽  
Model Control

This paper proposes a novel Internal Model Control (IMC) method for the control of superheater steam temperature. The IMC is known for good robustness including handling of systems with time delays. While Iterative Learning Control (ILC) is a strategy for dealing with periodic disturbances. By using a combination of IMC and ILC, their individual advantages are used to increase the robustness against modeling uncertainties and handling time as well as decreasing the influence of the periodic disturbances affecting the superheater steam. Some simulation examples are shown to illustrate performance improvements that can be achieved by the new method over the conventional IMC and PID methods. Key Words: Superheater Steam Temperature; Internal Model Control; Iterative Learning Control

Comparison of Damped Velocity and Acceleration Control for Non-Redundant Manipulators

2000 ◽  
Author(s):  
Yu-Che Chen ◽  
Kevin A. O’Neil
Keyword(s):  
Path Tracking ◽  
Least Square ◽  
Dynamics Simulation ◽  
Control Algorithms ◽  
Redundant Manipulators ◽  
Velocity Control ◽  
Redundant Manipulator ◽  
Singular Configurations ◽  
On Line ◽  
Simulation Results

Abstract Damped Least Square (DLS) method has been widely used as an on-line algorithm for manipulator path tracking near and at singular configurations. Wampler (1986) formulated the framework of DLS method applied to velocity control and addressed the applicability of DLS method to acceleration control. The purpose of this paper is to demonstrate the differences in the joint paths generated by damped velocity and damped acceleration control algorithms in non-redundant manipulators. We examine these joint paths, find the cause of the differences, and demonstrate the features of damped acceleration control in non-redundant manipulator dynamics. Simulation results on a planar 2R and a spatial 6R manipulator moving through and near singular configurations verify the phenomena analyzed.

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