On Algorithms For Fast Control And Perturbation Reduction Of Agitator Tank Based On Neural Dynamics

With the rapid development of electronic information technology and computer technology in recent decades,automation control technology has also made rapid progress.In the chemical industry,agitator tank plants are often used,and in order to achieve higher production targets,plants need to continuously improve the production efficiency of agitator tank plants.Specifically,the agitator tank should be formulated to ensure that the concentration of the solution in the tank reaches the expected value quickly and that the actual level of the solution in the agitator tank is not higher than the pre-determined level warning line.These goals are all achieved based on the control algorithm of the agitator tank.The traditional control algorithm has the disadvantage of low solving rate and easy to be disturbed by external perturbations,furthermore,the agitator tank equipped with the traditional control algorithm has low production efficiency and the accuracy is susceptible to external perturbation fluctuations.Therefore,the design of a new agitator tank control algorithm became an urgent problem in the chemical industry.In this paper,a neural dynamics based study of fast control and perturbation reduction algorithms for agitator tanks is performed.The algorithm significantly increases the production rate of the reagents produced in the mixing tank,allowing the concentration error of the mixed solution to converge quickly to zero.Specifically,the agitator tank output solution concentration can be approximated to the desired concentration over a short period of time,and the trajectory of solution level changes in the tank can rapidly converge to the desired trajectory.In addition to this,the mixing tank may be subject to external disturbances during actual operation.For example,the import and export of raw materials and reagents are partially blocked,and the agitator tank is shaken considerably.These external perturbations will most likely cause the final formulation of reagents in the mixing tank to fail,resulting in a waste of chemical resources.In order to compensate for the deficiencies of the existing agitator tank control algorithm,this paper presents a neural dynamics based improvement of the existing agitator tank control algorithm.Solving the perturbation problem also inevitably consumes additional computing time,which in turn reduces the actual production efficiency of the mixing tank.agitator tanks using the control algorithm presented in this paper have shorter response times and good perturbation reduction capabilities compared to agitator tanks using existing control algorithms.The main research work and innovative content of this paper is as follows:1.A fast control algorithm for agitator tanks is proposed.This control algorithm has a faster response time than existing control algorithms.Based on the neural dynamics model,we give the design steps of a fast control algorithm for the agitator tank,which can effectively control the amount of feedstock input to the agitator tank.Afterwards,the theoretical analysis of the fast response performance of the agitator tank system equipped with this fast control algorithm was carried out,and comparative simulation experiments were conducted on the agitator tank equipped with the proposed algorithm and the original control algorithm.The experimental results verify the correctness of the theoretical analysis and further confirm the rapidity of the fast control algorithm.2.Agitator tanks are often disturbed by perturbations when operating in the field and existing control algorithms do not have the ability to reduce perturbations.Therefore,when agitator tanks using conventional control algorithms are subjected to external perturbations,it is likely to affect the accuracy of the parameters associated with the reagents produced.Therefore,a agitator tank-based destructive algorithm is proposed in order to enhance the destructive capability of the agitator tank.We further investigated the theoretical aspects of the perturbation reduction performance of the mixing tank system equipped with this control method.Then the computer is used to conduct comparative simulation experiments,and through the simulation experiments,the proposed control method is finally verified to have the practical significance of perturbation reduction.3.A neural dynamics based agitator tank fast control and de-perturbation algorithm is proposed.The control algorithm takes into account the perturbation problems encountered by the agitator tank in practice as well as the latency problems that arise when dealing with perturbation problems.The proposed control algorithm provides better fast response performance and noise reduction compared to existing algorithms.The error convergence performance and noise reduction performance of the solution parameters of the agitator tank containing this control algorithm were analyzed from a theoretical perspective.Subsequently,simulation experiments were conducted by computer,and the experimental results verified the superiority of the controller.