Traditional simulators predominantly operate with position control at specific frequencies and largely neglect the appropriate imposition of accelerations on the structure. This restricts the application of realistic accelerations during fatigue testing and reduces the fidelity of tests to real road conditions. This study proposes an integrated experimental–analytical framework for motorcycle testing under laboratory conditions. Within the framework, smooth displacement reference signals are generated from noisy field-measured acceleration signals through Fourier-based harmonic curve fitting and analytic integration. Subsequently, a nonlinear adaptive backstepping control algorithm is designed to ensure accurate replication of these references within the 0–25 Hz bandwidth under parametric uncertainties. This approach provides a valuable and repeatable alternative to conventional on-road testing, ensuring that realistic road-induced accelerations are accurately imposed on the motorcycle structure during fatigue testing. Experimental signals were collected from a motorcycle on three different road surfaces, and the performance of the generated reference signals was evaluated in both the time and frequency domains. Experiments conducted on a real-time industrial controller demonstrated that the proposed controller exhibits superior tracking performance across all road profiles, achieving a Root Mean Square Error (RMSE) as low as 1.3 mm, while the Fourier-based reconstruction achieves $R^2$ values approaching 0.97. The controller maintains consistent precision and negligible performance variance despite significant differences in road characteristics, thereby offering a controlled and cost-effective laboratory simulation alternative to conventional on-road durability testing.

Accurate replication of road signal effects over the vehicles in laboratory environments is critical for vehicle durability testing and development. However, the traditional signal reconstruction methods often suffer from the inclusion of noise in the collected acceleration data. Thus, there is a limitation on the fidelity of hydraulic road simulations. This study proposes a comprehensive experimental-analytical framework for motorcycle testing in a laboratory environment. In the study, the integration of Fourier-based curve fitting with nonlinear adaptive control algorithms was done. Experimental signals were initially collected from a motorcycle on three different road surfaces. The displacement reference signals for the hydraulic actuators were generated using a harmonic curve-fitting approach from these signals. The performance analysis of the reconstruction signals was investigated in both the time and frequency domains. To ensure accurate trajectory tracking performance under parametric uncertainties, an adaptive backstepping control algorithm was designed. Experimental results revealed the superior performance of the proposed controller at all three road profiles, achieving Root Mean Square Errors (RMSE) as low as 1.3 mm. The controller exhibited robustness, maintaining consistent tracking precision with negligible performance variance across significantly different road characteristics, thereby validating the framework's utility for fatigue analysis.