As a vital component in energy transmission, the safety of oil pipelines is closely tied to the stability and efficiency of energy supply. In crude oil transportation pipeline, impurities such as wax and water tend to deposit or adhere to the inner walls of pipelines, which will reduce the flow efficiency and even lead the blockages. On the other hand, defects such as corrosion and cracks will also occur after the long-term operation of the pipeline. Therefore, regular pigging and inspection are essential to maintain pipeline integrity and ensure safe operations. In these procedures, precise control of the pipeline inspection gauge (PIG) speed is critical-not only to enhance cleaning efficiency but also to minimize risks associated with improper speeds. To address external disturbances during pigging operations-such as pipeline deformation, circumferential welds, and pressure fluctuations-this study proposes an adaptive control strategy based on the nonlinear backstepping method. This strategy, grounded in the Lyapunov stability theory and SR model, enables the design of a dynamic controller capable of accurately predicting and adjusting the PIG’s speed in real time. By modulating the opening of a bypass valve and altering the flow area, the controller can effectively regulate the pressure differential across the PIG, maintain its velocity within the optimal range for efficient cleaning and reduce the influence of external interference. A comparative simulation model was developed in Simulink Toolbox to evaluate the performance of the proposed nonlinear backstepping controller against a conventional PID controller. The results indicate that the nonlinear method yields faster response and superior control precision. Further simulations on inclined and curved pipelines demonstrate that the adaptive controller reliably predicts variations in PIG velocity and displacement, adjusting control actions accordingly. Overall, the nonlinear backstepping adaptive control strategy ensures rapid speed stabilization even under complex conditions, offering enhanced responsiveness, robustness, and adaptability. This approach provides a promising solution for improving the efficiency and safety of pigging operations in real-world pipeline systems.