Abstract
The current study employs a novel nonlinear robust control approach for path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties in the vertical plane. Firstly, a nonlinear underactuated AUV model is established to characterize the dynamics of AUV and path-following error. To resolve dependence on a detailed model that appeared in previous studies, the unknown time-varying attack angular velocity in the dynamic model of the path-following error is considered as the kinematic uncertainty, while the linear superposition of the external environmental disturbances, the perturbations in the internal model parameters, and other unmodeled dynamics in the dynamic model is chosen as lumped dynamic uncertainties. Several reduced-order extended state observers (ESOs) are designed for estimating both of these uncertainties. Secondly, to reduce the impact of input saturation and avoid the “explosion of complexity” associated with traditional back-stepping method, a nonlinear track differentiator (NTD) is utilized to follow the virtual control signal and its derivative. Thirdly, the constructed reduced-order ESOs and NTD are adopted to establish an augmented back-stepping controller, where its ability to stabilize the overall system is demonstrated using the Lyapunov theorem. Finally, extensive simulations and analyses in various working conditions, including the nominal working condition without disturbances, the working condition with multiple uncertainties, and the conditions which better replicate the actual environment, are performed to demonstrate the effectiveness, superiority, and robustness of the designed controller.
Highlights
With the growth of marine operations, autonomous underwater vehicles (AUVs) have become valuable tools in various applications, including water quality control, geo‐logical sampling, underwater archaeology, underwater rescue, and oceanographic sur‐veys [1,2,3,4,5]
In order to maintain stability and improve robustness in the presence of multiple disturbances, many control strategies such as back‐stepping control [13,14], sliding mode control (SMC) [15,16], fuzzy control [17], neural network control [18,19], observer‐based method [20,21], model predictive control (MPC) [22], and their combinations [23,24] have been widely utilized for path‐following control of underactuated AUVs
Jective of the current research can be described as follows: Considering the dynamics of AUV and path‐following error dynamics described by Equations (2) and (4), design an appropriate controller which can force the underactuated
Summary
With the growth of marine operations, autonomous underwater vehicles (AUVs) have become valuable tools in various applications, including water quality control, geo‐. In order to maintain stability and improve robustness in the presence of multiple disturbances, many control strategies such as back‐stepping control [13,14], sliding mode control (SMC) [15,16], fuzzy control [17], neural network control [18,19], observer‐based method [20,21], model predictive control (MPC) [22], and their combinations [23,24] have been widely utilized for path‐following control of underactuated AUVs. In [25], an adap‐. In recent years, extended state observer (ESO) has been well developed to estimate the internal model uncertainties and/or the external environmental disturbances [29,30].
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