Research On Self-tuning Method Of Control Parameters Of Crane System Based On Swarm Intelligence Algorithm

The bridge crane system is an underactuated nonlinear system,which is widely used in industrial places such as port,workshop,warehouse and construction site.With the increasing automation in all industries,higher demands are being placed on the anti-swing performance of bridge cranes and it is therefore necessary to design a complete and intelligent control solution for bridge crane system.As the performance of the controller is closely related to the controller parameters,parameter tuning is very important in the controller design process.However,the uncertainty of the effect of the controller gain on the performance of the controller and the interaction between the parameters to be tuned limit the performance that can be achieved.Because of the simplicity of the swarm intelligence algorithm,the fact that it does not require gradient information and has the advantage that it is easy to jump out of the local optimum,it is widely used in various disciplines or engineering applications to solve parameter tuning problems,so this thesis uses the swarm intelligence algorithm to tune the crane controller gain.The main elements of this thesis are as follows:(1)In order to realize the fast and accurate positioning control and stable anti-swing control of the bridge crane,the Lagrange method is used to establish the mathematical model of the bridge crane system,and the principle of PID and sliding mode controller applied in the bridge crane system and the structure of the control system are described.(2)To solve the problem of complex parameter setting of closed-loop controller and great influence on control performance,an off-line self-tuning method of controller gain based on improved African vulture algorithm was proposed.First,in order to make the African vulture algorithm have a more comprehensive global search ability in the early stage,quasi-opposition learning was applied to the population initialization process and exploration stage to improve the population diversity Secondly,in the algorithm exploitation stage,the differential evolution operator based on adaptive parameters is introduced to balance the exploration and exploitation stage of the algorithm,so as to improve the convergence speed and result accuracy.Finally,the African vulture algorithm is applied to the controller parameter tuning,and the performance of the proposed control method is verified by simulation and experiment.(3)Aiming at the poor robustness of the controller of the bridge crane system with fixed parameters when the working state changes,an online self-tuning method of the bridge crane control parameters based on the sunfish algorithm is proposed.This method uses a new swarm intelligence algorithm-sunfish algorithm,ITAE index is selected as the fitness function of the algorithm,adaptive control parameters are obtained to replace the fixed parameters,and the system performance is updated in real time.The sunfish algorithm builds the corresponding mathematical model according to the biological habits of sunfish.The algorithm has low time complexity and good exploration and development ability.