• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.248-255
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• 2000

Whereas the linear PID controller is widely used for control of industrial servo systems a high precision positioning system is not easy to control only with the PID controller due to uncertain nonlinear dynamics such as friction backlash etc. As a viable means to overcome the difficulty a learning control scheme is proposed in this paper that is simple and straightforward to implement. The proposed learning controller takes full advantage of current feedback capability of the inner-loop of the control system in that electrical motor dynamics as the well as mechanical part of X-Y positioning system is included in the learning control scheme, The experimental results are given to demonstrate its feasibility and effectiveness in terms of convergence precision of tracking and robustness in comparison with the conventional control method.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.518-521
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• 1995

In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.185-190
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• 2009

A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control expert system based on flexible iterative learning control (F-ILC). Finally, a feedback compensation unit is incorporated against irregular heavy disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.402-406
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• 1989

Since the control parameters of classical PID controller are fixed for all control period, it is not easy to produce a desired transition phenomena. We incorporate an iterative learning scheme to the linear controller so that it has more flexibility and adaptation capability especially in the transition period. In this paper a hybrid type learning controller is proposed in which fixed linear controller guides learning at the beginning stage. Once a perfect learning is achieved, then the control action is performed by only the learning controller. A computer simulation result demonstrates better performance during transition time than that with only linear PD controller.

• Journal of the Korean Home Economics Association
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• v.50 no.8
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• pp.125-135
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• 2012

The purpose of this study is to examine the mediating effects of self-determined motivation between mother's psychological control and self-directed learning ability in children. The participants were 457 sixth-grade elementary students in the Gyung-gi province. They completed questionnaires that included the Self-Directed Learning Readiness Scale, K-SPQ-A, Psychological Control Scale. Descriptive statistics and Pearson's product correlation coefficients were obtained using SPSS (version 18.0), and tests of the mediation were performed using SEM with AMOS (version 18.0). The major findings of this study were as follows that significant correlations among maternal psychological control, self-determined motivation and self-directed learning exist. Also a mother's psychological control was negatively related to a child's self-directed learning. The relationship between maternal psychological control and a child's self directed learning was fully mediated by self determined motivation. These results suggested that high maternal psychological control was negatively affected that development of self-determined motivation and self-directed learning.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

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

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.1
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.