Composite Learning Finite-Time Control for Nonlinear Suspensions of Heavy-Duty Vehicles Under Varying Loads

This paper proposes a finite-time adaptive backstepping active suspension control strategy, integrating command filtering and composite learning, to address the degradation of ride comfort and attitude stability in heavy-duty vehicles caused by shifting loads and harsh roads. First, a nonlinear dynamic vehicle model is established, treating multi-source complex disturbances as a single lumped disturbance and accounting for suspension stiffness and damping nonlinearities. To stabilize the body attitude, a tri-axis controller governing the vertical, pitch, and roll motions is developed, incorporating the practical physical constraints of actuators. By employing a composite learning Radial Basis Function neural network, the controller achieves smooth approximation and precise compensation of lumped disturbances, significantly enhancing the system’s active disturbance rejection performance under complex excitations. Furthermore, the finite-time stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Finally, the strategy is evaluated under a 40% load mass mismatch and continuous random road excitations. Results indicate that the proposed strategy effectively curbs the deterioration of suspension nonlinearities during overloads, ensuring smoother dynamic transitions across all three axes. Compared to conventional backstepping control, the proposed approach reduces the root mean square values of vertical, pitch, and roll accelerations by 19%, 13%, and 35%, respectively. Ultimately, this framework effectively improves vehicle stability and disturbance rejection, providing a robust reference for heavy-duty vehicle chassis control.