Data-Enabled Predictive Control for Stability Improvement of Articulated Vehicles

Articulated vehicles are susceptible to instability issues due to their distinct dynamic properties. Most existing control strategies focus on constructing an integrated model, yet an accurate parametric model for a complex nonlinear system might be unavailable. To address this, a bilevel control structure is established, with the upper level generating corrective yaw moments and the lower level focusing on control allocation, then data-driven predictive control method is introduced, which relies only on input/output measurements to construct a non-parametric representation of the system, this method is implemented in a receding-horizon manner similar to MPC, incorporating constraints to achieve safe maneuvering. The effectiveness of the proposed controller is presented by simulation results, which further confirm its potential in vehicle dynamics control.