Model-based PD neural network control for pipe crack sealing manipulator

This study focuses on the control of a pipe crack sealing manipulator (PCSM) for concrete pipe crack sealing, with the capability to maneuver through horizontal and vertical pipes. This PCSM is a tree-type robot with five different branches. Observation and simulation studies indicate that only the fifth branch of the PCSM effectively executed the repair operation. However, in real application, the movement of these links is hindered by disturbances and uncertainties, preventing them from behaving as intended. Hence, a robust control scheme is essential to effectively manage the links of a pipe crack sealing manipulator. To address this need, this study introduces a Model-based PD neural network control (MBPDNNC) algorithm, which is then compared against modal-based PD (MBPD), Sliding mode control (SMC) with constant plus proportional rate reaching Law (CPPRL), and super twisting SMC (STSMC). The simulation study reveals that MBPDNN is characterized by greater accuracy and less chattering compared to the contrast control methods. The main contribution of this work is the compensation of disturbance by updating the weight by using the Radial basis function neural network (RBFNN). Through observation, it is evident that the neural network yields more promising results in the presence of disturbances and uncertainties.