Adaptive Speed Control of Climbing Robots Using Modified MRAC: From Simulation to Real-Time Application

Abstract

Climbing robots that operate on vertical steel surfaces are essential for maintenance and inspection in high-risk or hard-to-reach environments such as bridges, ships, storage tanks, pipelines, power plants, and wind farms. These robots must perform demanding tasks while ensuring safety, mobility, and reliability. A major challenge is achieving accurate motion control under significant disturbances, including magnetic adhesion forces, gravitational effects, and surface friction, which influence motor dynamics and system performance. This study presents the development and implementation of a motor positioning strategy for a differential drive climbing robot equipped with magnetic wheels. The control system is based on a model of motor dynamics, with parameters identified through experimental testing. Smooth reference trajectories are generated using curve-based functions, and a model reference adaptive control strategy is employed to enhance trajectory tracking under varying conditions. This hybrid technique combines a simplified reference model with adaptation that allows real-time adjustment for dynamic changes without relying solely on mathematical modelling, making it suitable for uncertain and variable environments. Model reference adaptive speed controllers, along with a modified version, are proposed, designed, and validated through both simulation and real-time experimental testing. The results demonstrate that the adaptive control strategy ensures precise speed regulation while effectively addressing practical challenges such as control signal saturation. These findings indicate that the proposed controllers can serve as core components of the positioning strategies for differential-drive climbing robots. Overall, the study highlights the value of integrating these methods into the control architecture of advanced robotic systems operating in uncertain environments.