迅速的姿态稳定在NanoCubeSat中更具挑战性,因为其容量最小,体重,能源,和体积受限的架构。固定增益非自适应经典比例积分微分控制方法无法在严重的环境干扰下提供低地球轨道的最佳姿态稳定性。因此,在仿真环境中开发了具有模糊推理设计的人工神经网络,以通过根据空间扰动对比例微分控制器进行自主增益调整来控制CubeSat的角速度和四元数。它阐明了带有反作用轮和低地球轨道中断的CubeSat姿态模型的动力学和运动学,即,重力梯度扭矩,大气扭矩,太阳辐射扭矩,和剩磁转矩。提出的ANFIS-PD控制方案的有效性表明,CubeSat保留了基于初始四元数的三轴姿态可控性,转动惯量,欧拉角误差,姿态角速率,角速度率与PID相比,ANN,和RNN方法。仿真结果表明,所提出的控制器方案实现了最小的均方根误差,从而导致辊的快速稳定。螺距,和偏航轴分别在20s的仿真时间内。
Prompt attitude stabilization is more challenging in Nano
CubeSat due to its minimal capacity, weight, energy, and volume-constrained architecture. Fixed gain non-adaptive classical proportional integral derivative control methodology is ineffective to provide optimal attitude stability in low earth orbit under significant environmental disturbances. Therefore, an artificial neural network with fuzzy inference design is developed in a simulation environment to control the angular velocity and quaternions of a
CubeSat by autonomous gain tuning of the proportional-derivative controller according to space perturbations. It elucidates the dynamics and kinematics of the
CubeSat attitude model with reaction wheels and low earth orbit disruptions, i.e., gravity gradient torque, atmospheric torque, solar radiation torque, and residual magnetic torque. The effectiveness of the proposed ANFIS-PD control scheme shows that the
CubeSat retained the three-axis attitude controllability based on initial quaternions, the moment of inertia, Euler angle error, attitude angular rate, angular velocity rate as compared to PID, ANN, and RNN methodologies. Outcomes from the simulation indicated that the proposed controller scheme achieved minimum root mean square errors that lead towards rapid stability in roll, pitch, and yaw axis respectively within 20 s of simulation time.