• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.4
• /
• pp.92-98
• /
• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.3
• /
• pp.9-15
• /
• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.6
• /
• pp.1122-1127
• /
• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.352-355
• /
• 1986

In this paper, some candidates suggested for the improvement of the robustness in adaptive control systems are shortly surveyed. Using dead zone concept of error in adaptation process, gain retardation methods such as .sigma.-modification and parameter restriction method are those considered. Feedforward compensation and normalized adaptation technique are also considered. New modeling technique suggested by Donati et al is used for the indirect control of plants containing unmodeled dynamics. The frequency band of input signal, which is used as a test signal and control signal simultaneously, is directly related to the control of plants containing high frequency parastics. Computer simulation results of the some selected algorithms are shown.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.93-95
• /
• 2006

In many control system applications, the system designed must not only satisfy the damping and accuracy specifications, but the control must also yield performance that is robust to external disturbance and parameter variations. We have shown that feedback in conventional control systems has the inherent ability of reducing the effects of external disturbance and parameter variations. Unfortunately, robustness with the conventional feedback configuration is achieved only with a high loop gain, which is normally detrimental to stability. The design of intelligent, autonomous machines to perform tasks that are dull, repetitive, hazardous, or that require skill, strength, or dexterity beyond the capability of humans is the ultimate goal of robotics research. This paper prove the robust control using Analog Adaptive Resonance Theorv(ART2) Algorithm about case study.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.425-426
• /
• 2006

This study proposes a new simulation method of high speed rotor system with the dynamic model using multi body dynamic analysis tool and with a new phase modulating technique as a system control algorithm. A dynamic model of high speed rotor system was built by, ADAMS, commercial multi body dynamic program. The phase modulating technique is a new control algorithm for a rotor system. This algorithm can control system using an adaptive proportional gain and an adaptive phase which are obtained from periodical input signal. To make control system, a ADAMS model and component parameters and phase controller was composed by Matlab Simulink And simulate it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2001.10a
• /
• pp.397-400
• /
• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.165-170
• /
• 2017

This paper presents an event-triggered model predictive controller for adaptive cruise control system with sampled and quantized-data. Unlike existing works, a longitudinal continuous-time model is used for the predictive control of the system. To efficiently utilize network resources, event-trigger scheme is employed, which allows limited sensor and actuator signal satisfying the condition that the measurement of errors is over the ratio of a trigger level. The proposed control gain is obtained by solving a convex problem satisfying several linear matrix inequalities at every sampling times. Simulation results are given to show the effectiveness of the proposed design method.

• ETRI Journal
• /
• v.25 no.6
• /
• pp.437-444
• /
• 2003

We derive a new joint power and rate control rule with which we can minimize the mean transmission delay in CDMA networks for a given mean transmission power. We show that it is optimal to respectively control the power inverse-linearly and the rate linearly to the square root of channel gain while maintaining the signal-to-interference ratio at a constant. We also show that the proposed joint power/rate control rule achieves excellent performance results in terms of the probability of the instantaneous delay being within a target delay against one-dimensional control schemes.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.10 no.6
• /
• pp.889-897
• /
• 1986

The purpose of this study is to describe the strategy of machining process suitable for developing adaptive control with constraint of NC-machine tool. The algorithm that controls machining process parameters of every sampling time is established for the constraint of torque in machinig center. To prove this AC algorithm, manual AC-unit control test is used for simulating the on-line AC strategy control. Also machining tests are carried out on a CNC-machining center fitted with the ACC system and compared with the simulated results. The practical effectiveness of the ACC systems so discussed and the reduction of machining time are demonstrated with reference to typical models of cutting workpieces. As a typical model, taper and step geometry model are selected. The computer simulation results have a good agreement with the experimental observation and make it possible to develope a NC-machine tool with an on-line ACC system.