Abstract
In the presence of the uncertain system dynamics, unknown time-varying disturbances and rudder saturation, this paper develops a novel robust adaptive course control scheme for unmanned surface vehicle (USV). Considering the characteristics of the rudder servo system, a double loop course controller of the practical and concise is proposed by the enhanced trajectory linearization control (TLC) technology. The key features of the developed controller are that, first, the neural networks are employed to online approximate unmodeled dynamics, and adaptive techniques are adopted to deal with completely unknown external disturbances; second, auxiliary systems that are governed by smooth switching functions, are developed in an unprecedented manner to compensate for the saturation constraints on actuators. The main innovation can be summarized as that the TLC technology is applied to the USV motion control field as a new control algorithm, and the enhanced technology based on traditional TLC not only reduces the number of adjustment parameters but also has simple structure and high robustness. Furthermore, a low frequency learning method improves the applicability of the algorithm. The stability analysis is established using the Lyapunov theory. Simulation results and comparison verify the effectiveness of the proposed strategy.
Highlights
In the recent years, there has been a growing interest in the development of unmanned surface vehicle (USV) due to its advantages of being fast, small volume and low cost
The control objective is to develop a practical and concise course control law for USV with unknown system uncertainties, unmodeled dynamics and rudder saturation so that the actual course ψ tracks the desired course ψd, and the course control law can ensure the boundedness of all signals in the whole system
Both rudder saturation and unknown time-varying disturbances can be compensated by constructing auxiliary dynamic system and adaptive technique, respectively, and unmodeled dynamics are approximated and canceled out by employing NNs
Summary
There has been a growing interest in the development of unmanned surface vehicle (USV) due to its advantages of being fast, small volume and low cost. The paper [25] investigates the path following control problem for an unmanned airship, in which a robust adaptive radial basis function neural network (RBFNN) is employed to handle unknown wind and uncertainties It does not consider a time-varying wind disturbance. Motivated by the existing results, considering the rudder servo system, a robust course control strategy is proposed to address the model uncertainties, unknown bounded disturbances and input saturation using trajectory linearization control (TLC), NNs and adaptive technique. It is proved the proposed strategy makes USV follow and keep desired course with arbitrarily small error.
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