Space manipulator trajectory tracking control method 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 track 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 have been further extended. However, the space environment has the characteristics of microgravity, high vacuum, strong radiation, and large temperature difference. In such a dangerous environment, it has become the unanimous goal of all space powers in the world to use space manipulators to assist or replace astronauts to complete a large number of difficult and dangerous tasks. [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 manipu...

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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 radial basis neural network to propose an adaptive neural network control method without manipulator dynamic model [7] , but did not discuss the solution when the model has cross-scale features; Xie Jian et al. proposed a neural network adaptive control method for floating-based space manipulators. Deterministic upper bound, the proposed adaptive control law guarantees the boundedness of the weights [8] , but the designed adaptive law is more complicated, 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 base space manipulator system [9] , Leiting designed a neural network adaptive control method for the floating-based space manipulator system with limited control torque [10] , however, the compensation laws designed by these two methods for parameter changes contain all the information of the dynamic model, in which the nominal part of the model is known information, which is a redundant part in the compensation law

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