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首页> 外文期刊>International Journal of Advanced Robotic Systems >Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network
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Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

机译：基于神经网络的不确定性自由浮动空间机器人的自适应鲁棒控制

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摘要

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.
机译：对于诸如电流测量技术和外部干扰等不确定因素，很难获得自由浮动空间机器人的精确数学模型。因此，提出一种合适的解决方案是针对自由浮动空间机器人的基于神经网络的自适应鲁棒控制方法。建立了自由漂浮空间机器人的动力学模型。设计了基于精确模型的转矩控制器，该控制器可以保证系统的稳定性。然而，在实践中，不能精确地获得系统的数学模型。因此，提出了一种神经网络控制器来逼近系统中的未知模型，从而避免了对数学模型的依赖。权重的自适应学习定律旨在实现在线实时调整。自适应鲁棒控制器设计用于抑制外部干扰并补偿逼近误差，并提高系统的鲁棒性和控制精度。基于李雅普诺夫理论证明了闭环系统的稳定性。仿真试验验证了所提控制方法的有效性，对自由浮动空间机器人具有重要意义。

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