Design of a hierarchical-clustering CMAC-PID controller

• Published in: 2017 International Joint Conference on Neural Networks (IJCNN) pp. 1333 - 1338
• Main Authors: Yuntao Liao, Koiwai, Kazushige, Yamamoto, Toru
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Niobium
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2017.7966007

Proportional-integral-derivative (PID) control algorithm is playing an important role in industrial control process. However, for nonlinear control objects, it is difficult to obtain the desired control performance by using a typical PID controller. Therefore, some intelligent PID controllers are proposed and one of them is a PID controller with some cerebellar model articulation controllers (CMACs). In this controller, CMACs are utilized as a control parameter tuner and PID control parameters are calculated as the output of CMAC. The CMAC obtains higher accuracy by increasing the number of label for each weight table while larger memory is needed and generalization ability decreases. On the other hand, the CMAC costs less memory while obtains higher generalization ability and the accuracy decreases. Hence, a novel CMAC that number of label for each weight table can be decided respectively is proposed so that the accuracy is compatible with the generalization ability. Moreover, the efficiency of the memory allocation is improved. The proposed method employs hierarchical clustering to perform specified numbers of label for each weight table. At last, the effectiveness of proposed method is verified by applying to a nonlinear system numerically.