Reinforcement learning-driven nonsingular fast terminal sliding mode control for pipe crack sealing manipulator

Abstract

This study explores control challenges associated with the Pipe Crack Sealing Manipulator (PCSM), a tree-shaped robot designed to seal cracks within concrete pipes. Equipped with five specialized branches, the PCSM adeptly navigates both vertical and horizontal pipe environments. A detailed CAD model was developed in SolidWorks, and data were meticulously exported to Simulink for refined control operations. SolidWorks simulations revealed that the PCSM’s fifth branch excelled in extensive crack repairs. The control of pipe crack sealing manipulators presents significant challenges due to uncertain dynamics, random disturbances, signal delay, and sensor noise inherent in confined environments. To overcome these issues, this study proposes a reinforcement learning–driven nonsingular fast terminal sliding mode control (RLNFTSMC) framework that enhances robustness, precision, and adaptability for crack sealing operations. The implemented RLNFTSMC strategy, includes an equivalent part utilizing actor-critic neural networks for dynamic estimation and a switching part that treats the mass matrix as a lumped value. Comparative analysis with nonsingular fast terminal sliding mode control (NFTSMC), adaptive NFTSMC, radial basis function neural network control (RBFNNC), and neural network-enhanced nonsingular fast terminal sliding mode control (NENFTSMC) shows that RLNFTSMC significantly improves the PCSM’s reliability and effectiveness in environments marked by random disturbances and unknown dynamics.