Improved Active Disturbance Rejection Control Based on Kalman Filter for Fast Steering Mirrors
Objective As an optical beam pointing control device,fast steering mirrors(FSMs)are crucial components of essential equipment used in various fields such as aerial imaging,laser communication,and space exploration.An FSM driven by a voice coil motor has the advantages of a large stroke and low driving voltage,and it is easy to control.Quadrant detectors(QDs)have been used in FSM systems as angle sensors due to their low cost and wide measuring range.However,QDs are greatly affected by both Johnson noise and background light noise,resulting in large measurement noise.An active disturbance rejection controller(ADRC),which can effectively estimate and compensate for disturbances and unmodeled dynamics,has been applied to FSMs to improve tracking performance.Large measurement noise contaminates estimations and degrades disturbance rejection performance.Large measurement noise thus poses a significant challenge in controlling FSMs.Therefore,improving the tracking performance and disturbance rejection capabilities of FSMs driven by voice coil motors with relatively larger measurement noise is critical.Methods An improved ADRC(IADRC)was proposed by combining a Kalman filter with a model-assisted extended state observer(MESO).First,the effects of the selected gain of the extended state observer on the performance of the ADRC were analyzed and revealed a trade-off between disturbance rejection and noise rejection(Fig.3-4).Second,a model identification method based on the Hankel matrix was used to identify the exact model of the FSM(Fig.5).An IADRC was then designed(Fig.6)that primarily consisted of a Kalman filter,model-assisted ADRC,and zero-phase error tracking controller(ZPETC).The Kalman filter was used for noise filtering,and the necessary signal was input to the MESO.The MESO-observed lumped disturbance was then added to the Kalman filter state equation.The model-assisted active disturbance rejection controller was chiefly composed of an MESO under linear state error feedback control laws.The MESO was responsible for estimating system states and lumped disturbance,and the PD controller was designed according to the states estimated by the MESO.Finally,to improve tracking performance,ZPETC was introduced as a feed forward controller.Results and Discussion To verify the control effect,the FSM was controlled by the IADRC,ADRC,and disturbance observer(DOB),and control performance and disturbance rejection experiments were conducted on a dSPACE platform.The experimental results show that the IADRC significantly improves the tracking performance of the FSM in high-frequency ranges(Fig.9).The results also show that under a sinusoidal signal with an amplitude of 0.15° and frequency of 100 Hz as reference input,the tracking accuracy of the IADRC increases by 20.99％and 65.40％and the phase lag is reduced by 35.66％and 78.31％over those of the ADRC and DOB,respectively(Fig.10).The comparisons of tracking performance were made more general by using sinusoidal signals with the same amplitude and various frequencies as well as with the same frequency and various amplitudes as reference inputs.The experimental results demonstrate that IADRC outperforms both ADRC and DOB in terms of tracking performance,showing a maximum increase in tracking accuracy of 65.40％(Tab.1-2).Under the condition of zero input,a torque disturbance signal with an amplitude of 0.045° and frequency of 10 Hz is introduced,and the disturbance rejection performance of the IADRC is improved by 35.36％and 61.26％over those of the ADRC and DOB,respectively(Fig.11).The IADRC can realize the accurate estimation and suppression of disturbances in the presence of relatively large measurement noise,thus effectively improving the control performance of the FSM.Conclusions A new control method based on ADRC for an FSM system was proposed.The Kalman filter was integrated with the model-assisted ADRC to avoid control performance degradation caused by the MESO sensitivity to measurement noise.The lumped disturbance from the MESO was contained in the Kalman filter to achieve accurate estimation and rejection of the disturbance and to improve the disturbance rejection capabilities of the FSM system.The study showed that the IADRC can effectively improve the tracking performance and disturbance rejection capabilities of FSM systems and has high practicability in practical applications.
万方数据
维普
