Adaptive Asymptotic Tracking Control for the Dynamic Models of Differential-Drive Unmanned Ground Vehicles Under Parametric Uncertainties

This paper proposes a dual-loop layered control mechanism for the dynamic trajectory tracking of non-holonomic unmanned ground vehicles (UGVs). The proposed scheme enhances steady-state precision while guaranteeing parameter convergence under specified trajectory constraints. To tackle the underactuated constraints of Unmanned Ground Vehicles, the control mechanism is structured into kinematic and dynamic loops. Specifically, a kinematic controller is first synthesized to serve as a virtual control law, generating desired velocity commands. Subsequently, a layered adaptive control strategy based on the Immersion and Invariance technique is developed for the dynamic loop. This strategy integrates a parameter estimation layer, which utilizes tailored tuning functions to ensure the exponential convergence of estimation errors under the condition that the reference trajectory is not persistently vertical. A controller design layer is then responsible for uncertainty compensation. By decoupling parameter adaptation from control law synthesis, the proposed mechanism circumvents the structural limitations of the certainty equivalence principle. Theoretical analysis confirms that the proposed design achieves almost-global asymptotic tracking. Simulation results demonstrate that the mechanism resolves the imprecise parameter convergence inherent in traditional adaptive schemes, eliminates steady-state pose fluctuations during time-varying trajectory tracking, and achieves asymptotic convergence of tracking errors.