Data-driven prescribed performance control for satellite with large rotational component

In this paper, the high precision attitude control for satellite with large misaligned rotational component is investigated, proposing a novel model-free prescribed performance adaptive control (MF-PPAC) method. Based on the model-free adaptive control (MFAC) strategy, high-precision attitude control is achieved depending on the system measurement rather than the spacecraft mathematical model, which demonstrates the superiority in handling nonlinearity and uncertainty of the spacecraft system, especially for those with unbalance of the rotational component. Furthermore, the transient and the stable-state behavior of the tracking error using MFAC is constrained in the prescribed performance bounds and converges to zero gradually with the utilization of prescribed performance function (PPF) and error transformation method. Finally, the convergence and boundedness of the system error, the bounded-input bounded-output (BIBO) stability of the proposed MF-PPAC is demonstrated by the contraction mapping theory. Simulation and comparison with disturbance-observer-based PD control and MFAC illustrate the effectiveness of the proposed method.     

Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle

An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller.     

碳纤维环氧预浸料前热处理及储放老化对复材力学性能的影响

基于DSC测试数据,采用非模型拟合动力学方法 Flynn-Wall-Ozawa(FWO)对碳纤维环氧预浸料的固化过程进行研究,并将试验结果与模型预测进行对比。结果表明,采用FWO法能很好地预测预浸料的固化行为。同时,结合热隔膜成型工艺特点,将预浸料分别置于80℃和室温中进行分段处理,将经不同预处理的预浸料采用相同的固化工艺制作成复合材料板并进行力学测试。结果显示,随着80℃预加热时间的延长,层合板剪切强度逐渐降低,试样厚度增加,弯曲模量略微低,弯曲强度出现小幅度波动。室温老化结果表明,随着老化时间的延长,层合板的层间剪切性能和弯曲性能都存在小的波动,但无明显规律。     

Energy based 3D trajectory tracking control of quadrotors with model-free based on-line disturbance compensation

In this work, a Revisited form of the so-called Model-Free Control (R-MFC) is derived. Herein, the MFC principle is employed to deal with the unknown part of a plant only (i.e., unmodeled dynamics, disturbances, etc.) and occurs beside an Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) strategy. Using the proposed formulation, it is shown that we can significantly improve the performance of the control through the reshaping properties of the IDA-PBC technique. Moreover, the control robustness level is increased via a compensation of the time-varying disturbances and the unmodeled system dynamics. This on-line compensation capability is provided by the MFC principle. The problem is studied in the case of Multi-Input Multi-Output (MIMO) mechanical systems with an explicit application to a small Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) where a stability analysis is also provided. Numerical simulations have shown satisfactory results, in comparison with some other control strategies, where an in-depth discussion with respect to the control performance is highlighted by considering several scenarios and using several metrics.     

A model-free method for attitude estimation and inertial parameter identification of a noncooperative target

Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes.