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# Fuzzy logic part7

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### Fuzzy logic part7

1. 1. 55//1313//20132013 11 Fuzzy Systems Types of Fuzzy Logic Controllers Types of Fuzzy Logic Controllers
2. 2. 55//1313//20132013 22 Types of Fuzzy Controllers: - Direct Controller - Types of Fuzzy Controllers: - Direct Controller - © INFORM 1990-1998 Slide 85 The Outputs of the Fuzzy Logic System Are the Command Variables of the Plant:The Outputs of the Fuzzy Logic System Are the Command Variables of the Plant: Fuzzification Inference Defuzzification IF temp=low AND P=high THEN A=med IF ... Variables Measured Variables Plant Command Fuzzy Rules OutputFuzzy Rules Output Absolute Values !Absolute Values ! Types of Fuzzy Controllers: - Supervisory Control - Types of Fuzzy Controllers: - Supervisory Control - © INFORM 1990-1998 Slide 86 Fuzzy Logic Controller Outputs Set Values for Underlying PID Controllers:Fuzzy Logic Controller Outputs Set Values for Underlying PID Controllers: Fuzzification Inference Defuzzification IF temp=low AND P=high THEN A=med IF ... Set Values Measured Variables Plant PID PID PID Human OperatorHuman Operator Type Control !Type Control !
3. 3. 55//1313//20132013 33 Types of Fuzzy Controllers: - PID Adaptation - Types of Fuzzy Controllers: - PID Adaptation - © INFORM 1990-1998 Slide 87 Fuzzy Logic Controller Adapts the P, I, and D Parameter of a Conventional PID Controller:Fuzzy Logic Controller Adapts the P, I, and D Parameter of a Conventional PID Controller: Fuzzification Inference Defuzzification IF temp=low AND P=high THEN A=med IF ... P Measured Variable PlantPID I D Set Point Variable Command Variable The Fuzzy Logic SystemThe Fuzzy Logic System Analyzes the Performance of theAnalyzes the Performance of the PID Controller and Optimizes It !PID Controller and Optimizes It ! Types of Fuzzy Controllers: - Fuzzy Intervention - Types of Fuzzy Controllers: - Fuzzy Intervention - © INFORM 1990-1998 Slide 88 Fuzzy Logic Controller and PID Controller in Parallel:Fuzzy Logic Controller and PID Controller in Parallel: Fuzzification Inference Defuzzification IF temp=low AND P=high THEN A=med IF ... Measured Variable Plant PID Set Point Variable Command Variable Intervention of the Fuzzy LogicIntervention of the Fuzzy Logic Controller into Large Disturbances !Controller into Large Disturbances !
4. 4. 55//1313//20132013 44 Permasalahan pada Fuzzy Systems Permasalahan pada Fuzzy Systems • Mengapa menggunakan fungsi trapesium? Mengapa bukan sigmoid, phi atau segitiga? • Mengapa terdapat 5 nilai linguistik untuk suhu udara (Cold, Cool, Normal, Warm, dan Hot), sedangkan untuk kelembaban hanya terdapat 3 nilai linguistik (Dry, Moist, dan Wet)? • Mengapa terdapat perbedaan kemiringan pada fungsi trapesium untuk suhu udara, kelembaban , dan durasi pendinginan?
5. 5. 55//1313//20132013 55 Permasalahan pada Fuzzy Systems • Bagaimana cara mendefinisikan aturan fuzzy? Mengapa kalau suhu udara adalah Hot dan kelembaban adalah Moist, maka durasi pendinginannya adalah Medium? Mengapa bukan Short atau Long? • Pada sebagian masalah, terdapat seorang ahli: • sangat memahami tingkah laku variabel-variabel linguistik dan aturan yang ada  pendefinisian fungsi keanggotaan dan aturan fuzzy bisa dilakukan dengan mudah dan cepat. Permasalahan pada Fuzzy Systems • Ketika tidak bisa memahami masalah dan tidak bisa menemukan ahli:  pendefinisian fungsi keanggotaan dan aturan fuzzy memerlukan usaha keras dan waktu yang lama  Jika tidak tepat, maka performansi dari sistem fuzzy yang kita bangun menjadi tidak optimal. • Tetapi, cara pendefinisian kedua hal tersebut, bisa menggunakan Algoritma Genetika (AG) atau Jaringan Syaraf Tiruan (JST).  menemukan fungsi keanggotaan ataupun aturan fuzzy yang paling optimum
6. 6. 55//1313//20132013 66 End of Fuzzy Systems Topic Thank You