Neuroadaptive Natural Logarithm Sliding Mode Control for Nonlinear Active Suspension Systems

Abstract

A neuroadaptive controller based on natural logarithm sliding mode control (lnSMC) is proposed for an active suspension system with unknown nonlinear dynamics and uncertain model parameters. The proposed control scheme ensures that the controlled states are constrained within the desired bound of heave and pitch motions, thereby eliminating the need for trial and error in determining the lnSMC controller parameters. Moreover, the unknown nonlinear system dynamics are approximated by a radial basis function neural network (RBFNN), which updates its weights continuously in real-time. Considering the high degree of parameter uncertainties in suspension systems, an adaptive law based on a gradient algorithm with a projection operator is incorporated to estimate the unknown parameters (e.g., vehicle mass and mass moment of inertia). Simulation studies on a half-car active suspension model are carried out to evaluate the performance and robustness of the proposed controller under various road disturbances, including bumps and random road profiles. For comparative purposes, neuroadaptive controllers based on classical sliding mode and terminal sliding mode are designed as benchmark controllers. The simulation results indicated that the proposed controller achieves a better suspension performance indicators compared to the benchmark controllers.