Adaptive Single Input Tracking Controller for Parallel Robot System Based on Hybrid Brain Emotion and Cerebellar Model Articulation Control Network Using Wavelet Function
摘要
This research poses an important challenge in controlling nonlinear robotic systems due to uncertainty, which makes it difficult to determine the mathematical equations in the object’s model. This article proposes a method of using Wavelet Function Hybrid Controller (WFHC) to improve the precision of the robot control system. The WFHC controller is designed to adjust and learn from the system’s uncertain components automatically. The structure of the WFHC controller consists of two main subsystems: the Brain Emotional Learning (BEL) and the Cerebellar Model Articulation Control Network (CMAC). BEL and CMAC use the signed distance between the actual and sliding surfaces to process the input and overcome chattering from the SMC. The learning rules and parameters of the proposed controller are designed based on Lyapunov theory to ensure stability and global convergence of the control system. Eventually, the proposed structure is tested on a five-bar parallel robot system and compared with other control methods to evaluate the effectiveness and advantages of WFHC in actual environments. The results show that the MSE at