Abstract
The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.
Highlights
A mobile satcom antenna (MSA) is a kind of satellite communication antenna which can maintain continuous and reliable communications between satellites and users in movement
Comparisons are made between the results of the conventional PID method, the TSM in reference [19], the NTSM in reference [15], the non-singularity fast terminal sliding mode (NFTSM) in this paper, the radial basis function (RBF) neural network-based NFTSM
The paper focuses on the inertial sensor-based two gimbal mobile satcom antenna
Summary
A mobile satcom antenna (MSA) is a kind of satellite communication antenna which can maintain continuous and reliable communications between satellites and users in movement. According to the high order sliding mode algorithm proposed in [22], “chattering removal” can be achieved by combining the arbitrary-order sliding mode controller with the dynamic sliding mode Another well-known way to reduce chattering problem in TSM is to use tanh function instead of the sign function [23]. A radial basis function (RBF) neural network has several important features such as simple structure, fast learning and better approximation capabilities It has been adopted in TSM control systems [24,26]. A novel B-spline neural network with the capability of shape adjustment is proposed and applied to approximate the external disturbance and the unmolded dynamics of the system.
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