• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.67-71
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• 1988

We present an algorithm that uses trajectory following errors to improve a feedforward command to a robot in the iterative manner. It has been shown that when the manipulator handles an unknown object, the P-type learning algorithm can make the trajectory converge to a desired path and also that the proposed learning control algorithm performs better than the other type learning control algorithm. A numerical simulation of a three degree of freedom manipulator such as PUMA-560 ROBOT has been performed to illustrate the effectiveness of the proposed learning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.326-331
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• 1988

In this paper, an iterative leaning control algorithm for the linear discrete system is proposed. Based upon the parameter estimation method, the learning for good tracking control is acqured through a sequence of repetitive operations. A series of simulation are performed to show the validity of this algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.7
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• pp.743-751
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• 2013

In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.56-65
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• 1997

In this paper, we propose a reference input odification-type iterative learning control law for a class of discrete-time nonlinear systems and prove the convergence of the output error. We can get the high-precision in case of the trajectroy control when the proposed control law is properly combined with a feedback controller, and we can easily implement the learning control law compared to the control input modification-type learning control law. To show the validity and the convergence perfodrmance of the proposed control law, we perform experimentations on the trajectroy control and rejection of periodic disturbance for a 2-axis SCARA-type direct drive robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.577-583
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• 1994

The purpose of this paper is to study the force control law which can be implemented on a non-modified robot system. The external force control algorithm proposed in this paper can be designed by means of a classical and modern control law. We showed the validation and the possibility of muti-dimensional force control idea through the simulation and experiments. Also, the Iterative learning control is studied for compensating errors due to thr disturbances and nonlinear effects. The previous information(control input, error) was used to determine the control input of next trial. The experimental result show the vaidity of this algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.10
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• pp.888-893
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• 2000

This paper deals with the development of an automatic design software for DC servo motor control, which provides good performance with rapid response and velocity control accuracy. In the proposed method, the design is automatically executed using Matlab, and iterative learning control algorithms are used in the design process. We applied this method to 50W, 100W, 200W, 300W, 500W, 750W, 1.8kW and 4.5kW DC servo motors which are widely used in the industry. We compare the results of the manual tuning design method with that of the automatic design method presented in this paper. From the experimental results, we can find that the performance of the proposed method is better than that of the manual tuning design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.71-77
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• 1995

In this paper are proposed robust iterative learning control(ILC) algorithms for both linear continuous time-invariant system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_1
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• pp.521-531
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• 2022

This study proposed a new approach to intelligent control of a mobile robot system by back properpagation based on multi-layer neural network. A experiment result is given in which some artificial assumptions about the linear and the angluar velocities of mobile robots from recent literature are dropped. In this study, we proposed a new thinique to impliment the real time conrol of he position and velocity of mobile robots. With the proposed control techinique, mobile robots can now globally follow any path such as a straight line, a circle and the path approaching th toe origin using proposed controller. Computer simulations are presented, which confirm the effectiveness of the proposed control algorithm. Moreover, practical experimental results concerning the real time control are reported with several real line constraints for mobile robots with two wheel driving.