Application of learning to high-speed robotic manipulators

1998 ◽  
Vol 212 (4) ◽  
pp. 315-323
Author(s):  
A Kirecci ◽  
M J Gilmartin
Keyword(s):  
High Speed ◽  
Actuator Saturation ◽  
Learning Control ◽  
Advanced Technique ◽  
Control Techniques ◽  
High Speeds ◽  
Advanced Control ◽  
On Line ◽  
Physical Features ◽  
On Line Learning

When a desired signal is applied to a servo system it responds in a characteristic fashion and follows the required trajectory with an error. The physical features of the actuators and the gain setting of the controller are the main parameters that determine the response of the system. Controllers with fixed gain values are effective for many conventional processes using slow-speed manipulators. However, there are several cases where the precise tracing of a fast trajectory under different payloads requires more advanced control techniques. When the motion is cyclical, learning control is one advanced technique which is appropriate to use. Depending solely on measurements of data from the preceding cycle, its implementation in real time is both fast and efficient. In practice, however, it has been observed that learning can induce high-frequency ripples on the tuned command curve which with increasing iterations result eventually in the saturation of the system's actuators. In this study, the use of on-line learning control techniques is discussed and a new approach using digital filters is implemented to prevent actuator saturation from occurring when learning is applied. A planar robotic manipulator has been designed and built to investigate the practical problems of learning control, particularly when the system runs at high speeds.

Adaptive Nonlinear PD Learning Control for Robot Manipulators

2004 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang ◽  
M. M. Gupta
Keyword(s):  
Control Structure ◽  
Control Method ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Learning Control ◽  
Asymptotic Convergence ◽  
Simulation Studies ◽  
Pd Control ◽  
On Line ◽  
On Line Learning

A new control method, called adaptive nonlinear PD learning control (NPD-LC), is proposed for robot manipulator applications in this paper. The proposed control structure is a combination of a nonlinear PD control structure and a directly learning structure. Consequently, this new control method possesses both adaptive and on-line learning properties. One of the unique features of the NPD-LC algorithm is that the learning is based on the previous torque profile of the repetitive task. It is proved that the NPD-LC enjoys the asymptotic convergence for both tracking positions and tracking velocities. Simulation studies were conducted by comparing the proposed method with many other existing methods. As a result, it was demonstrated that the NPD-LC method can achieve a faster convergence speed. The proposed NPD-LC is robust and can be implemented for the control of robot manipulators.

Dynamic modeling of planar parallel robots considering passive joint sensor data

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 649-661 ◽  
Author(s):  
Asier Zubizarreta ◽  
Itziar Cabanes ◽  
Marga Marcos ◽  
Charles Pinto
Keyword(s):  
Dynamic Model ◽  
Dynamic Modeling ◽  
High Speed ◽  
Parallel Robots ◽  
Sensor Data ◽  
Control Techniques ◽  
Passive Joint ◽  
Model Based ◽  
Control Schemes ◽  
Advanced Control

SUMMARYModel-based advanced control approaches are needed to achieve high speed and acceleration and precision in robotic operations. These control schemes need a proper dynamic model. Many approaches have been proposed by different authors in order to obtain the dynamic model of these structures. However, most of them do not consider the possibility to introduce redundant sensor data. In this paper, a methodology for obtaining a compact dynamic model considering passive joint sensor data is proposed. The dynamic model is defined in compact and structured form, which makes it appropriate to be used in advanced control techniques.

An overview of robot force control

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 473-482 ◽  
Author(s):  
Ganwen Zeng ◽  
Ahmad Hemami
Keyword(s):  
Stability Analysis ◽  
Force Control ◽  
Control Strategies ◽  
Learning Control ◽  
Control Algorithms ◽  
Control Techniques ◽  
Advanced Control ◽  
Robot Force Control

This paper reports on the existing robot force control algorithms and their composition based on the review of 75 papers on this subject. The objective is to provide a pragmatic exposition with speciality on their differences and different application conditions, and to give a guide of the existing robot force control algorithms. The previous work can be categorized into discussion, design and/or application of fundamental force control techniques, stability analysis of the various control algorithms, and the advanced methods. Advanced methods combine the fundamental force control techniques with advanced control algorithms such as adaptive, robust and learning control strategies.

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Patterns of Implicit Learning Below the Level of Conscious Knowledge

2010 ◽  
Vol 24 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Juliana Yordanova ◽  
Rolf Verleger ◽  
Ullrich Wagner ◽  
Vasil Kolev
Keyword(s):  
Implicit Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Knowledge Generation ◽  
Sleep Stages ◽  
Implicit Processing ◽  
Implicit Processes ◽  
Late Night ◽  
On Line ◽  
On Line Learning

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

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