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The intelligent motion control technology of Autonomous Underwater Vehicle (AUV) is the basis of long-distance voyage for AUV. The motion control capability in vertical plane is an important technical for AUV to complete multifarious missions. So, it is important and realistic significance to research on motion control in vertical plane of AUV. Motion control problem of AUV in vertical plane, especially depth control problems is researched based on active disturbance rejection control (ADRC) method in this paper. The kinetic and dynamic model in vertical plane of AUV is established and simplified. The active disturbance rejection controller design method is introduced. Detailed algorithms of each part for active disturbance rejection controller are discussed according to separability principle of ADRC. Based on ADRC, the depth controller is designed and applied in AUV. At last, simulation results indicate that the method is feasibility and effectiveness for AUV depth control.

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

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