A small deviation in the time-delay of the image tracker is essential for improving the tracking precision of an electro-optical system, and for future advances in actuator technology. The core goal of this manuscript is to address issues such as tracking the controller time-delay compensation and the precision of an electro-optical detection system using an advanced filter design, a fire control modeling, and an anti-occlusion target detection system. To address this problem, a small deviation in the time-delay prediction and control method of the image tracker is proposed based on the principle of linear motion transformation. The time-delay error formation is analyzed in detail to reveal the scientific mechanism between the tracking controller feedback and the line-of-sight position correction. An advanced N-step Kalman filtering controller model is established by combining a line-of-sight firing control judgment and a single-sample training anti-occlusion DSST target tracking strategy. Finally, an actuator platform with three degrees of freedom is used to test the optical mechatronics system. The results show that the distribution probability of the line-of-sight measuring error in a circle with a radius of 0.15 mrad is 72%. Compared with the traditional control method, the tracking precision of the optimal method is improved by 58.3%.

High-precision tracking control is essential for multi-axis electro-optical systems (EOSs) to defense low-slow-small (LSS) moving targets. The inherent rotational inertia and nonlinear joint friction degrade the accuracy in both dynamic and low-speed tracking scenarios, necessitating model-based feedforward controllers that require precise parameter identification. To solve this issue, this study proposes a motor-coupled dynamic parameters set (MCDPS) identification framework that incorporates physical feasibility constraints, specifically designed for EOSs that lack joint torque sensors. An asymmetric nonlinear friction model is proposed to characterize the nonlinearity and direction-dependence of joint friction. Then, an Iterative Composite Least Squares (ICLS) algorithm is employed to identify the inertial and nonlinear friction parameters through iterative linear regression. A backpropagation neural network (BPNN) using joint position and load data as inputs is also proposed to further reduce the modeling error of joint nonlinearities. Experiments are performed using a multi-axis EOS to track typical joint trajectory and LSS simulated target. Results demonstrate that the proposed ICLS algorithm combined with the position- and load-related BPNN outperforms traditional identification methods. Additionally, the model-based feedforward controller is compared with proportional integral derivative (PID), extended kalman filter (EKF) and extended state observer (ESO) with sliding-mode control (SMC) approaches, showing a 21.29% improvement in tracking accuracy.

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method.

Hybrid reluctance actuators (HRAs), offering high thrust and compactness, are increasingly used in various nanopositioning applications. However, the motion ranges of HRAs are limited by their inherent electromagnetic nonlinearity. A novel tooth-shaped-yoke hybrid reluctance actuator (TSYHRA) is proposed in this article. The TSYHRA features a geometrically optimized yoke configuration designed to attenuate the positive correlation between the motion range and the air-gap thickness, thus directly suppressing actuator nonlinearity at its source. With the enhanced linearity, the effective working range of the TSYHRA-integrated nanopositioner can be significantly increased. Results show that, compared with conventional designs, the TSYHRA exhibits a 19.8% increase in thrust coefficient, and the linearity of the thrust coefficient–displacement relationship improves by a factor of 2.5. Meanwhile, a 13.1% decrease in negative stiffness is demonstrated, and the nonlinearity of negative stiffness is reduced by a factor of 2.7. Further comparative experiments show that the effective working range in terms of the open-loop stability boundary is increased from 0.8 to 1.0 mm, demonstrating the effectiveness and superiority of the proposed TSYHRA.