Path Tracking of Highway Tunnel Inspection Robots: A Robust Enhanced Extended Sliding Mode Predictive Control Approach

The irregular geometry of highway tunnel linings, combined with uneven terrain and external disturbances, often causes inspection robots to deviate from their predefined paths. Due to the strong coupling inherent in robotic systems, these deviations propagate to the end-effector, significantly compromising automated inspection accuracy and effectiveness. To tackle these issues, this study introduces an Enhanced Extended Sliding Mode Predictive Control (EESMPC) method, which integrates an adaptive Extended State Observer (ESO). The algorithm is derived from the robot chassis model and a desired trajectory error model, enabling precise contour profile tracking. Crucially, the integrated ESO actively estimates and compensates for unmodeled disturbances and system uncertainties within the state feedback, thereby enhancing both path tracking stability and precision. Comparative MATLAB simulations and experimental path tracking tests evaluated the performance against three other controllers. The results demonstrate that the EESMPC algorithm achieves superior tunnel lining tracking performance, exhibiting marked improvements in both tracking accuracy and system robustness. Consequently, this approach significantly enhances the automated inspection accuracy and operational efficiency of highway tunnel inspection robots.