Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control

• Published in: IEEE transaction on neural networks and learning systems 2024-05, Vol.PP, p.1-15
• Main Authors: Li, Li-Jiang, Chang, Xiang, Chao, Fei, Lin, Chih-Min, Huynh, Tuan-Tu, Yang, Longzhi, Shang, Changjing, Shen, Qiang
• Format: Article
• Language: English
• Subjects: Biological neural networks; Control systems; Double-loop recurrent neural network (DLRNN); Fuzzy logic; Fuzzy sets; neural network controller; Recurrent neural networks; Robots; self-organizing neural network; Type-2 fuzzy sets; Vectors
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2024.3397045

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.