In this study, a control algorithm that integrates a Deep Neural Network (DNN) adaptive control architecture with a robust disturbance rejection mechanism for the underactuated mechanical system referred to as the Futura Pendulum. The study has a hybrid learning structure that combines offline supervised pretraining of the DNN’s inner layers with an online adaptation law that updates the output-layer weights in real time. The adaptive mechanism is supported by a Lyapunov-based stability analysis, which guarantees the convergence of the tracking error and the boundedness of all closed-loop signals. The impact of DNN layer size on performance was investigated using standard indices (ISE, IAE, ITSE, and ITAE). The results show that the controller achieves stable tracking with minimal chattering, even under random disturbances. Numerical simulations validate the robustness, adaptability, and improved efficiency of the proposed control method, demonstrating its potential for real-time implementation in uncertain and dynamic environments.

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.

Demiryolu düzensizlikleri nedeniyle araçlarda oluşan titreşimler çeşitli olumsuz etkilere neden olmaktadır. Bu çalışmada, yüksek hızlı demiryolu araçlarının düşey titreşimleri ikincil süspansiyonlara paralel yerleştirilen manyetoreolojik damperler vasıtasıyla yarı aktif bir yöntemle kontrol edilerek bastırılması amaçlanmıştır. Kontrol yöntemi olarak, manyetoreolojik damperin ölçülemeyen parametrelerinin ve araca ait bazı diğer parametrelerin belirsiz kabul edildiği bir modelde sistem kararlılığını garanti edebilecek uyarlamalı kontrol tercih edilmiştir. Manyetoreolojik damperlerin ikincil süspansiyon sistemine paralel çalıştırılması ile düşük enerji tüketimine rağmen özellikle yolcu konforunun artırılması yönünde etkin sonuç alınabilmektedir. Önerilen kontrolörün başarısı, zaman ve frekans alanında pasif sistem cevapları ile karşılaştırılarak gösterilmiştir.

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.