Real-time predictive clunk control using a reference governor. (June 2023)

• Record Type: Journal Article
• Authors: Reddy, Prithvi; Shahbakhti, Mahdi; Ravichandran, Maruthi; Doering, Jeff
• Is Part Of: Control engineering practice. Volume 135(2023)
• Publication Date: 2023-06
• Subjects: Automotive backlash -- Clunk -- Drivetrain control -- Reference governor -- Adaptive control -- Optimization -- Uncertainty analysis; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2023.105489
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Backlash in the gears of an automotive drivetrain causes an unacceptable NVH phenomenon during torque delivery, called clunk, which adversely affects the drivability of a vehicle. Torque shaping control systems can help mitigate clunk but they may cause the vehicle to feel sluggish due to their conservative torque delivery during lash crossing and may be difficult to calibrate well for all use cases. In this work, we propose a novel, real-time predictive clunk controller using a soft landing reference governor approach, which helps in reducing clunk during lash crossing while simultaneously ensuring quick torque delivery. This model-based controller is easy to calibrate, and implemented in tandem with a shuffle controller and a backlash position estimator for providing smooth torque delivery during tip-in scenarios. Experimental data is used for developing a model, and the performance of the designed controller is verified through the experimentally validated model to meet impact velocity constraints in various use cases. Real-time capability of the controller is validated up to a sample time of 5 ms through processor-in-the-loop tests. The performance of the proposed controller is compared to a state-of-the-art model predictive clunk controller and to a conventional bang–bang clunk controller, and the benefit of using the reference governor-based clunk controller is demonstrated. The impact of model uncertainties on the performance of the clunk controller is analyzed and solutions for adapting to the uncertainties are demonstrated. Finally, the robustness of the controller is analyzed for various driver requested torque ramp rates and changes in the backlash size.