Search

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system's state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system's anti-interference ability and tracking accuracy. [ABSTRACT FROM AUTHOR]

This study presents a compound control algorithm that enhances the servo accuracy and disturbance suppression capability of direct drive components (DDCs). The servo performance of DDCs is easily affected by external disturbance and the deterioration of assembly characteristics due to a lack of deceleration device. The purpose of this study is to compensate for the impact of external and internal disturbances on the system. First, a linear state space model of the system is established. Second, we analyzed the main factors restricting the performance of DDCs which includes sensor noise, friction and external disturbance. Then, a fractional-order proportional integral (FOPI) controller was used to eliminate the steady-state error caused by the time-invariable disturbance which can also improve the system's anti-interference capability. A state-augmented Kalman filter (SAKF) was proposed to suppress the quantization noise and compensate for the time-varying disturbances simultaneously. The effectiveness of the proposed compound algorithm was demonstrated by comparative experiments, demonstrating a maximum 89.34% improvement. The experimental results show that, compared with the traditional PI controller, the FOPISAKF controller can not only improve the tracking accuracy of the system, but also enhance the disturbance suppression ability. [ABSTRACT FROM AUTHOR]