Intelligent Algorithm-based Optimization Design And Application Of Active Disturbance Rejection Controller

As an important subject of computational intelligence,for solving the problems of path optimization,intelligent scheduling control,and parameter optimization,some new ideas and means are provided by group intelligent optimization algorithm.Among them,Artificial Fish Swarm Algorithm and Particle Swarm Optimization algorithm are two representative algorithms in swarm intelligence algorithm.Because of their simple operation and excellent performance,they are widely used in many fields,but they inevitably have some shortcomings.Therefore,combining the characteristics of the algorithm to improve it,can not only improve the performance,but also expand the application areas,and it is of great significance to solve practical optimization problems.The Active Disturbance Rejection Controller(ADRC)is evolved from PID.It does not depend on the precise mathematical model of the controlled object and can give real-time estimates without the need to know the external disturbance model of the system.It has the advantages of simple implementation and robustness.However,in practical applications,due to the disadvantages of its many parameters and difficulty in setting,its application effect is not ideal.In this paper,ADRC optimization design and application are taken as an example.The improvement of hybrid algorithm composed of Artificial Fish Swarm Algorithm and Particle Swarm Optimization algorithm and its application in ADRC parameter tuning are studied.The main contents of this paper are as follows:Firstly,the composition and principle of the ADRC are studied.In combination with the principle analysis of the three components of the controller,the parameters that need to be set are determined.According to the idea of modular construction,the module building technology of S function is used to create the module library of ARDC,and a complete simulation example of the ADRC is built in Simulink.Secondly,the Artificial Fish Swarm Algorithm and Particle Swarm Optimization algorithm are selected to form a hybrid algorithm and improved.For the Artificial Fish Swarm Algorithm part,through uniform initialization,the distribution of the initial population is optimized.At the same time,the population is grouped according to the grouping method based on the frog leaping algorithm,and the different search strategies were used to improve the purpose and efficiency of the search;For the part of Particle Swarm Optimization algorithm,improved elite Gaussian learning is introduced to jump out of the optimal value stagnation and improve the accuracy of the final result.Through the standard function test,the performance of the improved algorithm is compared with other algorithms.Thirdly,the application of the improved hybrid algorithm in the parameter optimization of the auto-disturbance rejection controller is studied.According to the characteristics of the autodisturbance rejection controller,the integral of time absolute error and overshoot of the input are introduced into the system evaluation function,so that the tuning parameters meet the system requirements;the auto-disturbance rejection controller simulation based on the improved hybrid algorithm is built in MATLAB/Simulink.Through this experimental environment,the control performance of the Active Disturbance Rejection Controller by using the algorithm-optimized parameters can be tested.Finally,taking the control of UAV flight attitude as an example,the fixed-wing UAV is selected as the research object,and an ADRC-based UAV attitude control simulation platform and an X-plane-based simulated test flight environment are constructed.Attitude simulation experiments and simulation flight tests based on X-plane were carried out.Through the analysis of the experimental results,the effectiveness of the optimized design of the ADRC in the UAV attitude control was verified.