This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

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A kind of All-attitude north-finder using two-position measurement method is introduced in this paper. In such a system, one Dynamic Tuned Gym (DTG) and two accelerometers are used to sense earth rotating rate and the gravitational acceleration respectively. This system can work out azimuth precisely within three minutes. In the paper, following a theoretical analysis of ermr factors effecting north finding accuracy, an experiment to simulate north-finder work is done. Based on the error analysis and expaiment, the hardware and software which implement the control of turning electramator, power control of re-balancing circuit, the data acquisition of DTG and accelerometer's outputs, calculation of azimuth and long distance communication are designed. According to the final test, it is proved that the system has good repetition and high accuracy.

The control of nonlinear dynamics is gaining increasing attention since many practical systems are with such kind of characteristics. To deal with the system uncertainty, in this paper, the efficient learning control using neural network is proposed for the nonlinear strict-feedback system. The whole scheme is with the back-stepping design, while the novel learning is proposed for the neural network weights update. To deal with the approximation error, the robust item is added. The stability of the closed-loop dynamics is analysed and the effectiveness of the design is verified through flight simulation.