Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

游标万向节磁悬浮飞轮通常用于航天器的姿态控制和干扰抑制。由于圆锥形电磁轴承的特殊结构，由轴向磁力产生的径向分量和磁性气隙的变化将导致刚度和扰动的非线性。这不仅会导致悬架控制系统的稳定性差，还会导致转子位置的跟踪精度无法令人满意。为了解决系统的非线性问题，本文提出了比例积分微分神经网络控制方案。首先，建立考虑扰动和刚度参数非线性变化的转子模型。然后，通过梯度下降法在线调整神经网络的权重，以确保磁力的准确输出。最后，基于李雅普诺夫稳定性理论进行了收敛性分析。仿真结果表明，与一般的比例积分微分控制和径向基函数神经网络控制相比，该方法具有最高的跟踪精度和优良的稳定性。实验结果证明了理论分析的正确性和有效性。仿真结果表明，该方法具有最高的跟踪精度和优良的稳定性。实验结果证明了理论分析的正确性和有效性。仿真结果表明，该方法具有最高的跟踪精度和优良的稳定性。实验结果证明了理论分析的正确性和有效性。