Machine Learning-based Flexible Link Robot Control

نوع مقاله : مقاله پژوهشی

نویسندگان

1 بخش کنترل و ابزار دقیق شرکت نفت فلات قاره ایران، خارگ، بوشهر، ایران

2 گروه علوم کامپیوتر، دانشگاه تگزاس در ال پاسو، 79968، ایالات متحده آمریکا

چکیده

Fuzzy systems have recently attracted the attention of researchers and various methods based on fuzzy logic systems have been presented. This paper presents a novel adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very similar to ANFIS but in ANFIS2 a layer is added for purpose to type reduction. An adaptive learning rate-based backpropagation with convergence guaranteed is used for parameter learning. Finally, the proposed ANFIS2 are used to control of a flexible link robot arm. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time.

کلیدواژه‌ها


عنوان مقاله [English]

Machine Learning-based Flexible Link Robot Control

نویسندگان [English]

  • Ali Abdali 1
  • Verya Monjezi 2
1 Control Systems and Instrumentation Department of Iranian Offshore Oil Company, Kharg, Bushehr 7546143572, Iran
2 Computer Science Department, University of Texas at El Paso, TX79968, USA
چکیده [English]

Fuzzy systems have recently attracted the attention of researchers and various methods based on fuzzy logic systems have been presented. This paper presents a novel adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very similar to ANFIS but in ANFIS2 a layer is added for purpose to type reduction. An adaptive learning rate-based backpropagation with convergence guaranteed is used for parameter learning. Finally, the proposed ANFIS2 are used to control of a flexible link robot arm. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time.

کلیدواژه‌ها [English]

  • ANFIS
  • Interval Type-2 Fuzzy Sets
  • Inverse Control
  • Flexible Link Robot