• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.466-477
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• 2000

For developing a CMAC-based adaptive critic learning system to control the cart-pole system, various papers including neural network based learning control schemes as well as an adaptive critic learning algorithm with Adaptive Search Element are reviewed and the adaptive critic learning algorithm for the ASE is integrated into a CMAC controller. Also, quantization problems involved in integrating CMAC into ASE system are studied. By comparing the learning speed of the CMAC system with that of the ASE system and by considering the learning genemlization of the CMAC system with the adaptive critic learning, the applicability of the adaptive critic learning algorithm to CMAC is discussed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.28-37
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• 1999

Differentially Responsible Adaptive Critic Learning technique is proposed for learning the control technique with multiple control inputs as in robot system using reinforcement learning. The reinforcement learning is a self-learning technique which learns the control skill based on the critic information Learning is a after a long series of control actions. The Adaptive Critic Learning (ACL) is the representative reinforcement learning structure. The ACL maximizes the learning performance using the two learning modules called the action and the critic modules which exploit the external critic value obtained seldomly. Drawback of the ACL is the fact that application of the ACL is limited to the single input system. In the proposed Differentially Responsible Action Dependant Adaptive Critic learning structure, the critic function is constructed as a function of control input elements. The responsibility of the individual control action element is computed based on the partial derivative of the critic function in terms of each control action element. The proposed learning structure has been constructed with the CMAC neural networks and some simulations have been done upon the two dimensional Cart-Role system and robot squatting problem. The simulation results are included.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.384-389
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• 1998

본 논문은 특정 응용에 적합한 퍼지 제어기의 최적 설계 파라메터(퍼지 규칙과 소속 함수)를 찾는데 역전파 학습 과정과 유전 알고리즘을 결합한 Lamarckian 상호적응 기법을 이용한 뉴로-퍼지 제어기의 새로운 설계 방법을 제안한다. 설계 파라메타들은 진화에 의한 전역적 탐색을 통해 높은 포함값과 유용한 퍼지 규칙들을 갖는 규칙 베이스와 작은 근사화 오차와 좋은 제어 성능을 갖는 소속 함수들을 얻도록 제어기간 파라메타 조절을 수행하며, 학습에 의한 국부적 탐색을 통해 각 퍼지 제어기가 원하는 제어 결과를 나타내도록 제어기내 파라메타 조절을 수행한다. 제안한 상호적응 설계 방법은 유전 알고리즘의 모든 세대에서 역전파 학습이 이루어지므로 보다 좋은 근사화 능력을 나타나고, 사용한 무게 중심 비퍼지화기가 정확한 비퍼지화값을 계산하므로 보다 좋은 제어 성능을 가지며, 퍼지 규칙 베이스와 소속 함수들의 최적화 탐색 과정이 입출력 공간의 같은 퍼지 분할 상에서 통합된 적응 함수에 의하여 동시에 수행되므로 탐색을 위한 작업 공간이 아주 작아지는 장점이 있다. 시뮬레이션 결과는 Lamarckian 상호 적응에 의해 얻어진 FLC가 퍼지 규\ulcorner 수, 근사화 능력, 제어 성능등 모든면에서 다른 방법에 의해 얻어진 FLC보다 가장 우수함을 보여준다.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.11-35
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• 1995

In this paper the fuzzy extension for the classical engineering mechanics problems is studied. The governing differential equation is derived for the buckling loads of the columns with uncertain mediums: the their own weight and the flexural rigidity. The columns with one typical end constraint(hinged1 clarnped/free) and the other finite rotational spring with fuzzy constant are considered in numerical examples. The vertex method is used to evaluate the fuzzy functions. The Runge-Kutta method and Determinant Search method are used to solve the differential equation and determine the buckling loads, respectively. The membership functions of the buckling load are calculated. The index of fuzziness to quantitatively describe the propagation of fuzziness is defined. According to the fuzziness of governing factors, the varlation of index of fuzziness for buckling load is investigated, and the sensitivity for the end constraints is analyzed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.352-359
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• 1988

A learning control system which should have memory elements can be designed by utilizing the concept of parameter adaptation for unknown control object system parameters and regard it as a hybrid adaptive control system. A parameter adaptive learning control system applicable to a continuous time system has been already reported. Since there have been rapid developments in digital technology, it is possible to extend a continuous time parameter adaptive learning control system concept to a discrete time case. This problem is treated in this paper. Its justfication is proved and a simulation shows that this algorithms is effective.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 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
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• v.10 no.1
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• pp.88-95
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• 2006

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They we difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Journal of Korea Society of Industrial Information Systems
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• v.3 no.1
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• pp.183-191
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• 1998

다양한 산업분야의 생산공장에서 주로 활용되고 있는 6축 수직다관절로보트는 대부분 단순반복운동을 하고 있다. 단순반복중 point-to-point제어보다 품질을 요하는 tracking-to-trajectory 제어를 위한 분산학습제어에 대하여 연구하고자 한다. 관련 학습제어기법으로는 선형누적형기법과 간접적응기법이 있다. 두기법의 차이는 시스템 정보의 유무이며, 시스템의 주어진 상황에 따라 두 기법중 하나를 선택할 수 있다. 간접적응형 기법은 zero tracking error를 보장받기 위해서 보다 많은 반복을 요하는 경비를 부담하여야 한다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.03a
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• pp.125-128
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• 1998

다양한 산업분야의 생산공장에서 주로 활용되고 있는 6축 수직다관절보트은 대부분 단순반복운동을 하고 있다. 단순반복중 point-to-point제어보다 품질을 요하는 tracking -to-trajectory제어를 위한 분산학습제어에 대하여 연구하고자 한다. 관련 학습제어기법으로는 선형누적기법과 간접적응기법이 있다. 두 기법의 차이는 시스템의 정보의 유무이며 시스템의 주어진상황에 따라 두 기법중 하나를 선택할 수 있다. 간접적응형 기법은 zero tracking error를 보장받기 위해서 보다 많은 반복을 요하는 경비를 부담하여야 한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1895-1902
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• 2001

This paper gives an overview of the relationships between methods of loaming and adaptive control. It is the objective of this paper to develop adaptive learning control algorithms that combine the advantages of adaptive control with those of leaning control to the extent possible for the type of system model used. The robustness of this adaptive loaming control with respect to reinitialization errors and fluctuation of dynamics from disturbance is analyzed extensively. Simulation results have shown to verify the effectiveness of the proposed control algorithm.