Research On Parameter Self-tuning Method Of ADRC For Spatial Coarse Pointing Assembly

Laser communication is widely used in the field of space communication due to its high speed,low power consumption and strong confidentiality,which makes space laser communication technology an important technology in space exploration.As the main device to achieve coarse tracking in space laser communication,the coarse pointing assembly is the necessary mechanism to ensure the establishment of space optical communication link,which directly determines the quality of laser communication.Due to the large disturbance in the space environment,and the active disturbance rejection controller(ADRC),as an advanced control technology developed from the PID controller,has strong anti-disturbance ability and inherits the advantages of PID control that does not depend on the model.Therefore,this paper adopts the ADRC as the control strategy of the coarse pointing assembly.However,with the launch of the satellite by the coarse pointing assembly,the controlled object model may change due to the influence of external environmental temperature change,weight loss and other factors,and the change of mechanical structure characteristics of the motion mechanism with the increase of working time.This leads to the mismatch between the control parameters and the model,which further reduces the tracking accuracy of the on-board coarse pointing assembly compared with the ground test.Therefore,in order to make ADRC adapt to the change of controlled object model,it is of great practical significance to study the self-tuning method of control parameters.Firstly,the author investigates the existing parameter self-tuning methods of ADRC.According to the harsh requirements of the application scenarios for the algorithm,the self-tuning method based on evolutionary algorithm is selected.Through investigation and comparison,the grey wolf-particle swarm optimization algorithm in evolutionary algorithm is selected as the benchmark algorithm.Before introducing the main research work,the control technology of the coarse pointing assembly is introduced.The linear active disturbance rejection controller(LADRC)is selected as the controller of the coarse pointing assembly,and the grey wolf-particle swarm algorithm is used to parameter self-tuning simulation test of the LADRC.Secondly,aiming at the problem that grey wolf-particle swarm algorithm is easy to fall into local optimum,the controller is improved by model compensation.The transfer function of the controlled object is obtained by deducing the three-closedloop position control system model,and the model-compensated LADRC is designed on this basis.In order to verify the influence of the improved controller on the selftuning algorithm,the self-tuning simulation of the improved controller is carried out.The results show that in 50 Monte Carlo simulations,the mean value of the selftuning objective function decreases by 6.9% and the variance of the objective function decreases by 93.1% after the controller model is improved,indicating that the accuracy and robustness of the self-tuning algorithm are improved.By comparing the actual control effect of the worst setting and the optimal setting in 50 self-tuning simulations before and after the improvement,the beneficial effect of the improved controller on self-tuning is verified,and the control effect of the parameters obtained by self-tuning the improved controller is better.Thirdly,in order to further improve the reliability of the algorithm,a dimension reduction self-tuning method is proposed.The control gain is obtained by identifying the model.The remaining control bandwidth and observer bandwidth parameter are self-tuning by the grey wolf-particle swarm algorithm,thus reducing the parameter space dimension of the evolutionary algorithm.Before selftuning and,the lower bound of value is analyzed,and the lower bound ofis determined by combining the coarse tracking accuracy index required in the project,which is used as the boundary of self-tuning.Based on the improved controller,the simulation test of the reduced-dimensional self-tuning method is carried out.The results of 50 Monte Carlo simulations show that the mean value of the objective function of the reduced-dimensional self-tuning method decreases by4.7 %,and the variance decreases by 99.86 %,indicating that the reduceddimensional self-tuning method effectively improves the accuracy of the self-tuning results,and significantly improves the robustness of the self-tuning.We compare the control effect of the worst parameters in the 50 self-tuning of the dimension reduction method and the method before dimension reduction.The results show that the rise time of the parameters set by the dimension reduction method is shortened by 32.4 %,and the adjustment time is reduced by 15.8 %.The self-tuning parameters by the dimension reduction method have better control effect.Finally,experiments are carried out on the experimental platform.Ten repeated self-tuning experiments are carried out on the former controller and the improved controller,respectively.The experimental results verify the beneficial effect of the improved controller on self-tuning.In addition,the step response experiments are carried out with the worst parameters in ten self-tunings,respectively.The results show that the parameter control effect obtained by self-tuning the improved controller is better.On the basis of the improved controller,the reduced-dimensional self-tuning method is used for 10 repeated self-tuning experiments.The results show that the reduced-dimensional self-tuning method further improves the robustness of the parameter self-tuning compared with the non-reduced-dimensional method,which verifies the simulation conclusion.The step response of the worst parameters in the 10 times self-tuning of the reduced-dimensional method and the non-reduceddimensional method is compared,and the results show that the parameters set by the reduced-dimensional self-tuning method have better control effect.