The complex influence of various disturbances on an inertially stabilized platform (ISP) restricts the further improvement of its servo performance. This article investigates the mapping relationship between internal and external disturbances and servo performance by establishing a high-precision dynamics model of the servo device with simulation and experiment. For internal disturbances, a nonlinear model of friction and backlash is established based on a BP neural network, and the transmission error is reconstructed based on the principle of main order invariance. For external disturbances, the road disturbance torque, changing inertia, and mass imbalance torque are modeled. The quantitative mapping relationship between internal and external disturbances and servo performance is obtained through simulation, in which friction and road disturbance are the largest internal and external factors affecting the servo performance, respectively. These conclusions are verified by load simulation experiments on a certain type of servo device, in which the servo performance is improved by 17% for every 25% reduction of friction torque, and the servo performance is reduced by 12% for every 33% increase of road disturbance torque. The research results provide a reference for servo device selection, performance indicator assignment, and performance prediction of the ISP.

The dual-motor precision transmission mechanism (DMPTM) is an alternative way to eliminate backlash while ensuring the stiffness of the servo system. However, most of the established models of DMPTM are not accurate enough, and are not conducive to the optimization of system performance and the design of high-precision controllers. In this paper, based on the detailed linear model of the single components of the DMPTM, the dead-zone model, considering the time-varying stiffness, is proposed to describe the backlash of the two transmission chains, and the friction of the mechanism is depicted by the Stribeck model. Then, a high-precision dynamic model of the DMPTM is formed. Finally, the model validation experiments for the open-loop and closed-loop are carried out in the time domain and frequency domain. The experimental results show that the proposed model can accurately describe the nonlinear characteristics of the mechanism. The Pearson correlation coefficient between the proposed model and the actual system is $r_{\mathrm{open}-\mathrm{loop}}$ > 99.41%, for the open-loop, and $r_{\mathrm{closed}-\mathrm{loop}}$ > 83.7%, for the closed-loop, and these results are both better than those of the existing model. In the frequency domain, whether it is the open-loop or closed-loop model, the frequency response of the proposed model also reproduces the actual system well, which verifies the accuracy of the model.

In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.