• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.59 no.1
• /
• pp.29-34
• /
• 2010

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.11
• /
• pp.1819-1826
• /
• 1997

Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the force control of direct-drive robots. The proposed algorithm consists of feedback controllers and a neural network. After the completion of learning, the output of feedback controller is nearly equal to zero, and the neural network controller plays an important role in the control system. Therefore, the optimum retuning of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not require any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of the parallelogram link-type direct-drive robot.

• Knowledge Management Research
• /
• v.10 no.4
• /
• pp.31-42
• /
• 2009

With the rapid development of information technology, e-learning is growing in corporate. However, there are still problems in learning, such as low learning persistence rate. Learning outcomes are complex phenomenon influenced by a multitude of factors, it is need to considering the direct and indirect causal relationship among various factors. Therefore, the purpose of this study was to develop the causal model that explains the learning outcomes (satisfaction learning persistence) in corporate e-learning. This study was also intended to examine the causal relationship between the interaction and learning persistence through satisfaction mediators. For this, online survey was conducted with a sample of 270 learners who enrolled in cyber training course at A company. The major findings of this study are as follows: First, internality (internal locus of control, ${\beta}=.154$), interaction (${\beta}=.489$), satisfaction (${\beta}=.304$) have direct effect on learning persistence. Second, the interaction has direct effect on the satisfaction (${\beta}=.320$). Third, the satisfaction has direct effect on the learning persistence, and mediating the interaction and learning persistence. This result will contribute to build a learning strategy to improve learning outcomes.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.694-696
• /
• 2000

In this paper, the Direct Learning Control is applied to robot's trajectory tracking control to solve the problem that lies in the existing Iterative Learning Control(ILC) and the tracking Performance is analyzed and the better approach is searched using computer simulation and experiments. It is assumed that the Direct Learning Control(DLC) is saved onto memory basically after obtaining control input Profiles for several Periodic output trajectories using the ILC. In case the new output trajectory has special relations with the previous output trajectories, there is an advantage that the desired control input profile can be obtained without iterative executions only using the DLC. The robot's tracking control system is comprised of DSP chip. A/D converter, D/A converter and high-speed pulse counter included in the control board and the performance is examined by carrying out the tracking control for the given output trajectory.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.2
• /
• pp.76-80
• /
• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.3
• /
• pp.611-614
• /
• 2007

In this paper, a Direct Learning Control (DLC) method is presented to generate a virtual reference input for linear feedback systems to improve the output tracking performance. The original reference input is effectively modified by the DLC without any iterative learning process. The presented DLC is designed based on the information on the relative degree of a system and previously generated virtual reference inputs. It is illustrated by simulations that the virtual reference input generated by the proposed DLC can achieve high tracking performance, although the reference input cannot be appropriately shaped by using existing DLC methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1054-1057
• /
• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.369-372
• /
• 1993

In this thesis, we consider an iterative learning controller to control the continuous trajectory of 2 links direct drive robot manipulator and process computer simulation and real-time experiment. To improve control performance, we adapt an iterative learning control algorithm, drive a sufficient condition for convergence from which is drived extended conventional control algorithm and get better performance by extended learning control algorithm than that by conventional algorithm from simulation results. Also, experimental results show that better performance is taken by extended learning algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.3 s.174
• /
• pp.653-660
• /
• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.2
• /
• pp.19-25
• /
• 2003

For a class of multi-input multi-output nonlinear systems which perform a given task repetitively, an extended type of a direct leaning control (DLC) is proposed using the information on the (vector) relative degree of a multi-input multi-output system. Existing DLC methods are observed to be applied to a limited class of systems with the relative degree one and a new DLC law is suggested which can be applied to systems having higher relative degree. Using the proposed control law, the control input corresponding to the new desired output trajectory is synthesized directly based on the control inputs obtained from the learning process for other output trajectories. To show the validity and the performance of the proposed DLC, simulations are performed for trajectory tracking control of a two-axis SCARA robot.