In the presence of complex unknowns consisting of system dynamics and environmental disturbances, it is rather meaningful to exactly track an unmanned surface vehicle (USV) to the desired trajectory in practical scenarios including routing inspection, marine survey, and guard patrol, etc. In this paper, the exact trajectory tracking problem is solved by establishing a finite‐time extended state observer‐ (FESO) based exact tracking control (FESO‐ETC) scheme. By virtue of nonsmooth analysis, the FESO is firstly devised by only requiring continuous differentiability and is incorporated into the nonsingular fast terminal sliding mode control framework, and thereby further enhancing disturbance rejection and tracking accuracy. Moreover, global finite‐time stability of the entire FESO‐ETC closed‐loop system is derived from rigorously theoretical analysis, and thereby contributing to a model‐free finite‐time control paradigm. Simulation studies and comparisons demonstrate that the proposed FESO‐ETC approach can achieve exact trajectory tracking in the presence of complex unknowns.

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In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called “5-sv” model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the “5-sv” model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value.