Controlled multi-body dynamic simulation for structural characterization

Abstract

Virtual simulation is a popular tool used in the design of modern drivetrains, potentially offering a more efficient iterative design approach than hardware testing. For example, using feedback controls in mechanical system simulations allows testing alternative drivertrain models with different control strategies at a minimal cost. However, feedback control functionalities are seldom included in simulations for mechanical system design analysis and vice versa. Finding efficient ways that bridge the gap between the fields of structural design and feedback control design is of central interest for this project.

Phase Locked Loop (PLL) and Control Based Continuation (CBC) are feedback control architectures that recently gained popularity within the structural analysis community for characterizing nonlinear behavior. The basic analysis includes mapping of Frequency Response Curves (FRC) and the Backbone curve of the chosen dynamical system. PLL and CBC have been tested by the research community and are known to work, in both virtual and physical environments, for systems with relatively few Degrees Of Freedom (DOF). DOF can be defined as the minimum number of states required to model the system. A typical drivetrain model of today can have on the order of thousands of DOFs, depending on analysis purpose and chosen level of fidelity (higher fidelity models are typically more accurate but also use a larger number of DOFs). So, to deliver efficient feedback control of larger structural models, there is much scope for improvements among existing control architectures to satisfy industry requirements.

The thesis work aims to re-implement existing control architectures in a simple 1- DOF Duffing oscillator model. Another goal is to check whether the model-free controller used in the CBC control architecture can be replaced by a model-based controller. Model-based controllers use predicted system dynamics to generate optimal actuator signals. They provide better results, but are generally harder to implement. Finally, the process of integrating newly developed controllers into the existing drivetrain design workflow at Volvo Cars is explored.