• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.223-226
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• 2006

매니퓰레이션 기술 중에서 경로 계획은 중요한 문제 중의 하나이다. RRT는 경로 계획 알고리즘으로 최근에 제안되었다. RRT는 기존 알고리즘보다 빠르게 장애물을 회피하여 경로를 계획할 수 있다. 기존의 경로 계획 알고리즘은 그 상황에 따라 반복적으로 경로 계획을 하였다. 이러한 점을 개선하기위해 RRT와 인간의 소뇌구조를 모방한 CMAC을 결합한 CMACRRT를 제안한다. CMAC은 RRT가 만들어낸 경로와 그 상황을 기억하여 유사한 상황에서 경로를 다시 사용할 수 있게 해준다. 이렇게해서서 CMAC을 통해 학습된 상황에서 RRT를 사용하지 않고 기존의 경로를 사용할 수 있게 된다.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.45-51
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• 1995

This paper proposes a CMAC-based controller for servo systems with stick-slip friction. Performance of the controller was evaluated from computer simulations and compared with that of a conventional PID controller. Firction model used in the simulations is based upon the one proposed by Tustin. It was shown that the CMAC-based controller settles more quickly, and overshoots less than the PID. It was also shown that the CMAC is less sensitive to the changes of the plant parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.675-681
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, It is necessary to analyze the effects of the CMAC control parameters an the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with prespecified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• 한국기계연구소 소보
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• s.19
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• pp.125-139
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, it is necessary to analyze the effects of the CMAC control parameters on the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with pre specified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.289-295
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• 1998

본 논문은 빠른 학습과 정확한 근사 능력을 갖는 새로운 CMAC 신경망 기반 퍼지 제어기르 제안한다. 제안한 CMAC 신경망 기반 퍼지 제어기(CBFLC)는 한 학습 주기 동안 전향 및 역전파 연산시 신경망내 유닛중 극히 일부분만이 활성화되어 학습에 참가하므로 학습 시간이 매우 빠르고, 비퍼지화 연산시 소속 함수의 중심값 뿐 아니라 폭을 동시에 고려하여 정확한 근사화를 얻는다. 제안한 퍼지 제어기내 입？출력 소속 함수의 중심값 및 폭 등의 구조적 파라메터들은 역전파 알고리즘에 의해 갱신된다. 제안한 CMAC 신경망 기반 퍼지 제어기를 트럭 후진 주차문제에 적용하여 근사화 능력 및 제어 성능면에서 여러 다른 퍼지 제어기들과 비교한다.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3135-3141
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• 1996

In order to develop an efficient algorithm for S-to-M mapping in CMCA, characteristics of CMCA mappings is studied and conceptual mapping procedure is physically described. Then, careful observations on the mapping procedure and experience reveal a simple algorithm of the S-to-M mapping. The algorithm is described and compared with other procedures for S-to-M mapping. It is found very efficient in terms of computational operations and processing time.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.302-307
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• 1989

A pressure tracking control of hydroforming process is considered in this paper. To account for nonlinearities and uncertainties of the process, an iterative learning control scheme is proposed using Cerebellar Model Arithmatic Computer (CMAC). The experimental result shows that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.11-20
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• 1998

High-speed complex-number arithmetic units are essential to baseband signal processing of modern digital communication systems such as channel equalization, timing recovery, modulation and demodulation. In this paper, a new complex-number multiplication algorithm is proposed, which is based on redundant binary (RB) arithmetic combined with radix-4 Booth recoding scheme. The proposed algorithm reduces the number of partial product by one-half as compared with the conventional direct method using real-number multipliers and adders. It also leads to a highly parallel architecture and simplified circuit, resulting in high-speed operation and low power dissipation. To demonstrate the proposed algorithm, a prototype complex-number multiplier-accumulator (CMAC) core with 10-bit operands has been designed using 0.8-$\mu\textrm{m}$ N-Well CMOS technology. The designed CMAC core contains about 18,000 transistors on the area of about 1.60 ${\times}$ 1.93 $\textrm{mm}^2$. The functional and speed test results show that it can operate with 120-MHz clock at V$\sub$DD/=3.3-V, and its power consumption is given to about 63-mW.