Real-Time Nonlinear Model Predictive Control of a Virtual Motorcycle

Virtual prototyping tools are widely used in the development of new motorcycles, but the instability of two-wheel vehicles leads to the necessity of implementing a vehicle driver. In this brief, the development of an effective virtual rider (VR) is described that is capable of controlling the motorcycle during complex, high performance maneuvers. The control inputs are the steering angle, the lateral rider movement, and the throttle/braking effort. The proposed motorcycle model consists of a plane that can roll and slide (both in x- and y-directions), on which a moving point mass with lateral degree of freedom (DoF) is attached, representing the rider. The controller is designed within the nonlinear model predictive control (MPC) framework. The implementation is developed in MATMPC, an open-source toolbox, and tested in co-simulation with the commercial software VI-BikeRealTime, specifically designed to reproduce vehicles behavior with high fidelity. A chicane and a lap on a challenging track have been used to both show flexibility and evaluate performance, demonstrating the capability of the proposed VR of handling complex maneuvers.