In this paper, a new scheme of small integrated navigation system based on Micro Inertial Measurement Unit (MIMU), Global Position System (GPS) and Three-Axis Magnetometer (TAM) is presented. First the choice of these sensors and the structure of system are introduced respectively. Then the navigation computer, which uses TI high-performance floating-point nsp as core processor, is designed to realize both the data collecting and the navigation calculating. According to the error models of inertial navigation system, an integrated navigation algorithm used Kalman filter is proposed to fuse the information from all of the sensors. Finally, the prototype test results are presented to show the feasibility of the system.
Summary
In this paper, a command‐filter‐based fast finite‐time composite adaptive control scheme is proposed for nonlinear systems with input dead‐zone. The neural networks (NNs) are used to approximate the unknown functions of the controlled systems. The series‐parallel nonsmooth estimation models are used to predict the errors. In addition, the tracking error and prediction errors are anastomosed to update the weights of the NNs. To solve the problem of “explosion of complexity,” the command‐filter technology is adopted. Combining the Lyapunov stability theory and the adaptive backstepping algorithm, a composite adaptive backstepping control scheme is proposed. The proposed scheme dissolves the singularity problem which may emerge in the design process and the neural networks approximation performances can be realized as well. Meanwhile, the proposed scheme guarantees the tracking error converges into a small neighborhood of zero in fast finite‐time and the boundedness of all the closed‐loop signals. Finally, the simulation example is provided to verify the effectiveness of the proposed scheme.
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