지능 제어 시스템

1장 적응 시스템
1.1 소개 ··································································································· 12
1.2 신호의 특성 ························································································ 15
1.3 그래디언트(기울기) 알고리즘 ······························································ 21
1.4 이산 시스템 ························································································ 26
1.5 z 변환 ································································································ 30
1.6 Wiener filter ······················································································· 36
1.7 LMS 알고리즘 ····················································································· 42
1.8 RLS 알고리즘 ······················································································ 48
1.9 Kalman 필터 ······················································································ 63
1.10 시스템 인식 모델 구조 ······································································· 72
1.11 인식 모델 ·························································································· 75
2장 적응제어
2.1 소개 ··································································································· 82
2.2 모델을 기반으로 하는 적응제어 방식 ··················································· 84
2.3 Self-tuning adaptive control ································································ 87
2.4 시스템 변수의 평가 ············································································· 88
2.5 쌍적분 시스템의 MRAC 제어의 예 ······················································· 89
2.6 로봇의 적응제어 예 ············································································· 93
2.7 필터동역학을 이용한 적응제어 ··························································· 99
3장 신경회로망
3.1 단층 퍼셉트론 넷의 소개 ·································································· 106
3.2 비선형함수 ······················································································· 108
3.3 다층 퍼셉트론 넷 ·············································································· 110
3.4. 역전파 알고리즘 ·············································································· 116
3.5. 방사형 함수 기반 신경회로망 ··························································· 141
3.6. Recurrent Network(Feedback network) ··········································· 149
3.7 XOR 패턴인식 응용 ··········································································· 157
3.8 MATLAB 명령어를 이용한 신경회로망 ··············································· 163
3.9 패턴인식 예 ······················································································ 174
3.10 제스처 패턴인식의 이륜 로봇에의 응용 ············································ 177
4장 신경회로망 제어
4.1 소개 ································································································· 184
4.2 신경회로망을 이용한 시스템 인식 ······················································ 186
4.3 신경회로망의 역모델 직접 제어 ························································· 188
4.4 시스템 자코비안 ··············································································· 193
4.5 Feedback error learning(FEL) 방식 ················································· 195
4.6 Reference Compensation Technique(RCT) 방식 ································ 198
4.7 PID 이득값 튜닝 방식 ········································································ 204
4.8 역진자-수레 시스템 제어 예 ······························································· 214
5장 로봇의 신경망 제어
5.1. 소개 ································································································ 226
5.2. 로봇의 동적 모델기반 FEL 제어 ························································ 229
5.3. 로봇의 무모델기반 FEL 제어 ···························································· 234
5.4. 로봇의 모델기반 RCT 제어 ······························································· 240
5.5. 2축 로봇의 신경망 제어 응용 ···························································· 245
5.6. 뉴로 슬라이딩 모드 제어 ·································································· 274
5.7. 뉴로 시간지연 제어 ·········································································· 295
6장 퍼지논리
6.1 소개 ································································································· 316
6.2 퍼지 셋의 정의 ·················································································· 318
6.3 소속 함수 ·························································································· 324
6.4 소속 함수의 종류 및 특성 ·································································· 325
6.5 소속 함수 만들기 ·············································································· 327
6.6 퍼지셋 이론 ······················································································ 335
6.7 퍼지관계 ··························································································· 338
6.8 퍼지추론(fuzzy implication) ······························································ 341
6.9 퍼지 합성 ·························································································· 343
6.10 퍼지 제어 논리 ················································································ 347
6.11 Fuzzy Tool Box 사용하기 ······························································· 352
7 장 퍼지 제어 시스템
7.1 소개 ································································································· 358
7.2 입력의 퍼지화 ··················································································· 360
7.3 퍼지 제어 법칙(fuzzy rule) ································································ 365
7.4 퍼지 제어 법칙 만들기 ······································································ 374
7.5 퍼지 제어기 추론 ·············································································· 381
7.6 퍼지 집합 합성 ·················································································· 389
7.7 비퍼지화 ··························································································· 391
7.8 퍼지 제어 예 ····················································································· 398
7.9 역진자 제어 예 ·················································································· 400
8장 뉴로-퍼지 제어시스템
8.1 소개 ································································································· 408
8.2 뉴로-퍼지 소속 함수 ·········································································· 410
8.3 뉴로-퍼지 제어기 구조 ······································································ 412
8.4 TSK 뉴로-퍼지 제어기 ······································································ 419
8.5 퍼지 제어의 신경망 보상 방식 ··························································· 431
8.6 MATLAB명령어로 뉴로-퍼지 구현 ······················································ 435
8.7 역진자 시스템 제어예 ········································································ 440
8.8 로봇팔 제어예 ··················································································· 445
찾아보기 ······························································································ 449

신경회로망 제어 방식의 하나인 입력 경로에 보상하는 입력보상방식(RCT: Reference compensation technique)이 있다. 입력보상방식은 자코비안 정보가 필요하지 않고 신경회로망의 출력을 입력에 보상하여 시스템의 비선형성을 제거하는 제어 방식의 일종이다. 첫째로, 제어할 대상이 PD(Proportional and derivative)제어기로 먼저 안정된 후에 보상이 되기 때문에 안정성이 높다. 역모델 제어의 가장 기본 구조인 직접 제어방식은 안정성이 취약하다. 실시간 제어일 경우에 신경회로망에서 무슨 신호가 출력되는지 보장할 수 없으므로 로봇의 움직임에 대한 안정성을 보장할 수 없다. 신경망 제어기가 PD 제어기로 미리 안정된 시스템의 역모델을 인식하므로 제안한 제어방식의 안정성을 나타낸다. 둘째로, 불확실성을 보상하는 위치가 제어하는 시스템 밖에서 이루어지기 때문에 기존에 설치되어있는 실제 시스템들의 제어기들을 수정할 필요 없이 제안한 방식을 적용하기가 쉬운 이점이 있다. 특히 무선 RCT방식은 무인항공기 또는 드론의 제어에 유용하게 사용될 수 있다.