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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Researches on inverse simulation's applications in teleoperation rendezvous and docking based on hyper-ellipsoidal restricted model predictive control for inverse simulation structure
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Researches on inverse simulation's applications in teleoperation rendezvous and docking based on hyper-ellipsoidal restricted model predictive control for inverse simulation structure

机译：基于超椭球约束模型预测控制的逆仿真结构在逆操作交会和对接中的应用研究

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摘要

The inverse simulation is a technique used to calculate the control action based on the expected trajectory or experimental data and has been applied successfully in the aviation field. A novel inverse simulation technique is first introduced in the fields of rendezvous and docking. The least square estimation under the hyper-ellipsoidal restriction is presented to solve inverse simulation problems. The inverse simulation structure used in this paper is based on the model predictive control scheme. The predictive model is derived by Clohessy-Wiltshire dynamics equations, and the accurate one is obtained by two-body dynamics equations. When the designed trajectories or the data obtained by teleoperation console experiments are given to the predictive model, the inverse strategy is transformed into a generalized shrunken estimation problem. Then, the continuous control signals produced by the least square theory are dispersed by the pulse width modulation method and propagated into the high-order model. The inverse simulation results of the traditional control method and the inverse strategy mentioned in this paper show that the maneuvers can be better reproduced by inverse strategy, and the proper choices of sensitive factors and the receding horizon lead to better results.
机译：逆仿真是一种基于预期轨迹或实验数据来计算控制动作的技术，已成功应用于航空领域。首先在会合和对接领域中引入了一种新颖的逆仿真技术。为了解决逆仿真问题，提出了超椭圆约束下的最小二乘估计。本文使用的逆仿真结构基于模型预测控制方案。该预测模型是由Clohessy-Wiltshire动力学方程式推导出的，而准确的模型是由两体动力学方程式获得的。当将设计轨迹或通过远程操作控制台实验获得的数据提供给预测模型时，逆策略将转化为广义的收缩估计问题。然后，由最小二乘理论产生的连续控制信号通过脉宽调制方法被分散并传播到高阶模型中。本文所提到的传统控制方法和逆策略的逆仿真结果表明，通过逆策略可以更好地重现该策略，正确选择敏感因素和后退范围会带来更好的效果。

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