During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future.

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In this note, a novel multitechnique concise robust control scheme, based on the mirror mapping technique (MMT), closed-loop gain shaping algorithm (CGSA), Smith predictor (SP), and nonlinear feedback technique (NFT), is proposed for the pressure control of the liquefied natural gas (LNG) carrier insulation containment space. Firstly, a kind of integral unstable time-delay model is obtained by linearizing the nonlinear model of the pressure maintenance system around its equilibrium. By using the MMT and CGSA, one acquires the corresponding stable mirror mapping model of the unstable linear model and the designed controller. And the SP is introduced to tackle the time-delay problem. In addition, for the purpose of energy saving, the NFT is added into the control scheme. Finally, a set of experiments has been employed to illustrate the control effects, and the results show that it can achieve satisfying performance in aspects of disturbance rejection and energy saving.

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