Adaptive error-constrained tracking and collision avoidance of UUVs in complex environments

Considering the model uncertainties and the influence of obstacles on unmanned underwater vehicles (UUVs), the system performance and trajectory tracking accuracy may experience fluctuations or deviations. To address this issue, an adaptive error-constrained preset-time trajectory tracking control scheme is proposed. This scheme is designed to enable UUVs to avoid obstacles and accurately track reference trajectories in complex environments. First, an adaptive error-constrained preset-time obstacle performance function (PTOPF) is introduced, which dynamically adjusts based on the deviation from the reference trajectory, thereby enhancing the system's dynamic performance. In contrast, traditional methods typically employ static functions, making them less adaptable to complex environmental changes and system state fluctuations. Next, a preset-time extended state observer (PTESO) is developed to estimate model uncertainties and compensate for unavailable velocity information. While conventional approaches can handle such issues, they heavily rely on initial states or design parameters and have inherent limitations in predefined convergence time. Subsequently, a fast artificial potential field function (FAPF) with an introduced growth rate adjustment factor is proposed to guide the UUV in avoiding obstacles. Traditional APF methods can achieve a certain degree of obstacle avoidance; however, their response speed is relatively slow, making it difficult to react promptly to sudden environmental changes, which in turn affects the stable navigation of UUVs in complex scenarios. To address input saturation, a preset-time auxiliary dynamic system with a smooth switching function (PTADS-SSF) is constructed. Simulation results validate the effectiveness and advantages of the proposed control strategy in terms of trajectory tracking accuracy and obstacle avoidance capability. Specifically, the absolute error integrals for trajectory tracking in the surge and sway directions were reduced by approximately 22.65 % and 14.32 %, respectively, significantly improving the overall trajectory tracking performance of the system. • The designed PTESO can predefine the convergence time without relying on any initial state or design parameters. • The PTOPF can dynamically adjust the function range based on the error in deviation from the reference trajectory. • The FAPF introduces a growth rate adjustment factor. The growth rate of the FAPF can increase.