Torque ripples caused by cogging torque, flux harmonics, and current measurement error seriously restrict the application of a permanent magnet synchronous motor (PMSM), which has been paid more and more attention for the use in inertial stabilized platforms. Sliding mode control (SMC), in parallel with the classical proportional integral (PI) controller, has a high advantage to suppress the torque ripples as its invariance to disturbances. However, since the high switching gain tends to cause chattering and it requires derivative of signals which is not readily obtainable without an acceleration signal sensor. Therefore, this paper proposes a robust SMC scheme based on a rapid nonlinear tracking differentiator (NTD) and a disturbance observer (DOB) to further improve the performance of the SMC. The NTD is employed to providing the derivative of the signal, and the DOB is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. On the one hand, DOB can compensate the robust SMC speed controller, it can reduce the chattering of SMC on the other hand. Experiments were carried out on an ARM and DSP-based platform. The obtained experimental results demonstrate that the robust SMC scheme has an improved performance with inertia stability and it exhibits a satisfactory anti-disturbance performance compared to the traditional methods.

Due to the process defects and imperfection of drivers, permanent magnet synchronous motors (PMSM) are problematic to control. There is still a lack of effective high-performance control methods for inertial stabilized platforms based on PMSM currently. At present, the most frequently used method is sliding mode control (SMC), but traditional sliding mode control cannot overcome the contradiction between high performance and system chattering. In order to solve this problem and improve the system reliability and pointing accuracy, a new approach law for the sliding mode controller is proposed in this paper. In view of the large periodic torque ripple in PMSM, an iterative learning controller (ILC) is introduced to compensate for the disturbance. Based on these, aimed at suppressing all kinds of real-time disturbances in the working environment of the system, the extended state observer (ESO) is brought into the servo system to observe the lumped disturbance of the system, and the total disturbance observed is compensated into the sliding mode controller, so as to better suppress the system chattering and enhance the system’s ability of resisting external disturbance. Experiments are carried out on an inertial stabilization platform based on DSP + CPLD. The final experiments verify that the SMC with the new approach, combined with ILC and ESO, is of outstanding performance when compared with the traditional proportional integral (PI) + disturbance observer (DOB) control scheme.

In this paper, a MIMO (Multi-Input Multi-Output) fuzzy sliding mode control method is proposed for a three-axis inertially stabilized platform. This method is based on the MIMO coupling model of the three-axis inertially stabilized platform in which the dynamic coupling among the three frames, namely the azimuth frame, the pitch frame and the roll frame, is fully considered. Firstly, the dynamic equation of the three-axis inertially stabilized platform is analyzed and its linearized model is obtained. After this, the controller is designed based on the model, during which fuzzy logic is introduced to deal with the frame coupling and the adaptive fuzzy coupling compensation factor is designed to be part of the algorithm. A complete proof of the stability and convergence is also provided in this paper. Finally, the performance of the platform with a MIMO fuzzy sliding mode controller and PI controller is analyzed. The simulation results show that the proposed scheme can guarantee tracking accuracy and effectively suppress the coupling interference between the three frames.

In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, and the manipulation between features exacerbates the loss of information, leading to the degradation of precision and robustness, especially in the field of UAV target tracking. In response to this, we propose a new lightweight Siamese-based tracker, SiamCTCA. Its innovative cross-temporal aggregated strategy and three feature correlation fusion networks play a key role, in which the Transformer multistage embedding achieves cross-branch information fusion with the help of the intertemporal correlation interactive vision Transformer modules to efficiently integrate different levels of features, and the feed-forward residual multidimensional fusion edge mechanism reduces information loss by introducing residuals to cope with dynamic changes in the search region; and the response significance filter aggregation network suppresses the shallow noise amplification problem of neural networks. The modules are confirmed to be effective after ablation and comparison experiments, indicating that the tracker exhibits excellent tracking performance, and with faster tracking speeds than other trackers, these can be better deployed in the field of a UAV as a platform.

Fast steering mirror (FSM) plays a crucial role in stabilization of the line‐of‐sight (LOS) and phase shift compensation. The control accuracy of the FSM is affected by various disturbances especially the vibration in the aviation environment. Traditional anti‐disturbance methods, such as disturbance observer (DOB), have a little effect of suppressing disturbance in FSM. But it also brings some problem, such as increasing mass and amplifying high frequency noise. To solve these problems, an anti‐disturbance strategy based on adaptive robust control (ARC) was proposed. And it will not amplify the high‐frequency noise which is inevitable in DOB. Experimental results show that, using adaptive robust controller, the steady‐state error of the FSM decreased 4.8 times compared to simple PID control and 1.9 times compared to DOB+PID control in the simulated vibration environment.