Abstract
The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising.
Highlights
It is well-known that about 70% of our earth surface is covered by water, and there are many underwater areas that have not been discovered by humans yet
By bringing robust control and adaptive learning together, in this paper, we propose to employ the hierarchical sliding mode control (HSMC) technique to develop a robust control scheme for autonomous underwater vehicle (AUV)
In order to efficiently control motion of an autonomous underwater vehicle (AUV), we consider a model with six degrees of freedom (DoFs) including surge, sway, heave, roll, pitch and yaw [31,32], which present for both the position and orientation of the marine device
Summary
It is well-known that about 70% of our earth surface is covered by water, and there are many underwater areas that have not been discovered by humans yet. To maintain robustness in operations of an AUV given nonlinearities in its internal electromechanical systems and uncertainties caused unpredictable but unavoidable external disturbances, sliding mode control (SMC) has attracted much attention from practitioners, engineers and researchers [22]. The unknown, uncertain and nonlinear parameters can be learned through some adaptive strategies such as neural networks and fuzzy logic systems [25]. To handle nonlinearities and uncertainties in the AUV systems, it is proposed to exploit neural networks to adaptively learn the system parameters over time. In order to efficiently control motion of an autonomous underwater vehicle (AUV), we consider a model with six degrees of freedom (DoFs) including surge, sway, heave, roll, pitch and yaw [31,32], which present for both the position and orientation of the marine device.
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