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A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

This paper proposes an error analysis method based on epipolar geometry which is analyzed for improving the accuracy of attitude estimation. The influence of equivalent focal length and principal point coordinates on modeling error was analyzed by simulation and experiment based on a rotary table. The result shows that the influence of the equivalent focal length will increase as the rotation angle increases, the different motion states will lead to different degrees of impact of the principal point coordinates. Besides, we derived that pose measuring accuracy is unrelated to the magnitude of rotation angle and there is no accumulative error under simulation conditions. This proposed method can enhance the measuring accuracy and provide some meaningful advice for engineering applications.

Robust control of a class of uncertain Takagi-Sugeno (T-S) fuzzy system is investigated in the paper by using fuzzy region concept. The general uncertain T-S fuzzy model with Standard Fuzzy Partition (SFP) inputs is converted into fuzzy region ones. A Fuzzy Region Controller (FRC) is designed based on the fuzzy region concept, and sub-models in a fuzzy region share the same FRC. The relaxed stability conditions for closed-loop fuzzy region systems are derived by using Piecewise Smooth Quadratic (PSQ) Lyapunov function in terms of Linear Matrix Inequalities (LMIs). The derived stability condition, which only requires finding a local common positive definite symmetric matrix P in each operating region, can reduce the conservatism and difficulty in existing stability conditions. Finally, the feasibility and validity of this approach are demonstrated by a numerical example.