In this paper, a new third-order nonlinear dynamics solving the mismatched problem is proposed for double gimbal control moment gyros (DGCMGs) affected by friction and coupling torques, unmodeled dynamics, or parameter uncertainties; the new nonlinear dynamics with the control inputs and the disturbances into the same equations should not be linearized and any control method handling multivariate systems with cross-coupling between channels can be then used to design a controller. Secondly, a feed-forward neural network based observer with disturbance rejection ability is proposed to estimate both the exogenous/endogenous disturbances and the states of the system, the number of necessary sensors being decreased. Thirdly, in order to control the rotations of the inner/outer gimbals, compensate the disturbances, enhance the robustness of the control system, and handle the channel interferences, we design, evaluate, and compare two novel control architectures using the Lyapunov theory, the backstepping method or the dynamic inversion control technique. Each of the two control architectures uses two interconnected controllers (an inner gimbal controller and an outer gimbal controller), a neuro-observer, and two reference models to generate the necessary fictive control signals. The simulation results show that the proposed control approaches provide very good angular rate precision of the DGCMG system and successfully handle a wide range of disturbances, parameter uncertainties, and channel interferences. The smaller fluctuations of the rotation angles, angular rates, and angular accelerations in the gimbal channels indicate that the backstepping control provides better ability to reject disturbances, smaller overshoot and convergence time than the dynamic inversion control technique.

本文针对受摩擦和耦合扭矩，非模型动力学或参数不确定性影响的双万向控制力矩陀螺（DGCMG），提出了一种解决不匹配问题的新型三阶非线性动力学。具有控制输入和扰动进入相同方程式的新非线性动力学不应线性化，然后可以使用任何处理具有通道之间交叉耦合的多元系统的控制方法来设计控制器。其次，提出了一种基于前馈神经网络的具有干扰抑制能力的观测器，以估计外源/内源干扰和系统状态，同时减少了所需传感器的数量。第三，为了控制内/外平衡架的旋转，补偿干扰，为了提高控制系统的鲁棒性并处理信道干扰，我们使用Lyapunov理论，反步法或动态反演控制技术设计，评估和比较了两种新颖的控制架构。两种控制体系结构中的每一种都使用两个互连的控制器（一个内部云台控制器和一个外部云台控制器），一个神经观察者和两个参考模型来生成必要的虚拟控制信号。仿真结果表明，所提出的控制方法为DGCMG系统提供了很好的角速率精度，并成功地处理了各种干扰，参数不确定性和信道干扰。旋转角度，角速度，