Trajectory tracking control method for space manipulator based on cross-scale model

Abstract

The invention provides a space manipulator trajectory tracking control method based on a cross-scale model. In the case of analyzing the parameter and nonparameter cross-scale characteristics existed during the modeling of a free-floating space manipulator system, the manipulator joint space is subjected to real-time online tracking control. The control method introduces the radial basis neural network to approximate the variation item of the cross-scale characteristic in the dynamic model of the space manipulator and effectively inhibits the influence of the variation item on the system by means of the learning ability of the neural network, and designs the adaptive law to adjust the weight of the neural network in real time and performs the simulation verification by taking the plane two-connecting-rod space manipulator as an example, thereby realizing the fast and accurate tracking of the desired trajectory in the joint space of the space manipulator.

Description

technical field [0001] The invention belongs to the technical field of intelligent control and system simulation, and in particular relates to a trajectory tracking control method of a space manipulator based on a cross-scale model. Background technique [0002] With the continuous development of space technology, space exploration activities are further extended. However, the space environment has the characteristics of microgravity, high vacuum, strong radiation, and large temperature difference. In such a dangerous environment, the use of space robotic arms to assist or replace astronauts to complete a large number of arduous and dangerous tasks has become the unanimous goal of the world's space powers. [0003] A significant difference from the ground manipulator is that the base of the space manipulator is moving, which is a very complex multi-input-multiple-output strongly coupled nonlinear time-varying system, which makes the control problem of the space manipulator d...

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Problems solved by technology

However, the above robust control method is designed based on the upper bound of prior knowledge and is a relatively conservative control strategy, so it is not an optimal control strategy.

[0007] Guo Yishen and Chen Li used the radial basis neural network to propose an adaptive neural network control method without the dynamic model of the manipulator, but did not discuss the solution when the model has cross-scale features; Xie Jian et al. proposed a For the neural network adaptive control method of the floating-based space manipulator, the nonlinear function and the upper bound of the uncertainty of the model are approximated by the radial basis neural network. The proposed adaptive control law guarantees the boundedness of the weights, but the The designed adaptive law is relatively complex, which affects the calculation speed; Zhang Wenhui et al. designed a radial basis neural network robust adaptive control method, which is applied to the floating-based space manipulator system. Arm system, a neural network adaptive control method is designed. However, the compensation law designed by these two methods for parameter changes contains all the information of the dynamic model, and the nominal part of the model is known information. In the compensation law is a redundant part of

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