Composite learning based finite time nonsingular terminal sliding mode path tracking control for intelligent vehicles

Abstract

This paper addresses the path tracking control problem for intelligent vehicles subject to parametric uncertainties, unmodeled dynamics, and external disturbances. A composite learning-based finite-time nonsingular terminal sliding mode control (CL-FNTSMC) strategy is proposed. Unlike conventional adaptive sliding mode controllers that rely solely on tracking errors for parameter update—and thus require the restrictive persistent excitation condition-the proposed scheme incorporates a serial–parallel estimation model to construct prediction errors, which together with tracking errors drive a composite learning law. This mechanism ensures accurate online estimation of unknown parameters under the significantly weaker interval excitation condition. A nonsingular terminal sliding surface, constructed with a continuously differentiable nonlinear function, guarantees finite time convergence while inherently avoiding singularity. Furthermore, a nonlinear disturbance observer is integrated to estimate and compensate for lumped disturbances in real time, substantially enhancing robustness. Rigorous Lyapunov-based analysis establishes the practical finite time stability of the closed loop system. Comprehensive comparative simulations under aggressive disturbances and significant parametric uncertainties demonstrate that the proposed CL-FNTSMC achieves superior tracking accuracy, faster convergence, and markedly improved disturbance rejection compared with conventional NTSMC, adaptive fast NTSMC, and PID controllers. The results confirm that the proposed framework offers an excellent balance of fast transient response, high steady-state precision, and strong robustness.