Research On Intelligent PID Control Of Liquid Level System

The research process of the liquid level control system is generally carried out by using the PID algorithm with relatively simple structure and strong adaptability.However,with the continuous improvement of people's production and scientific and technological ability,the control system designed today is more and more towards the large-scale and complicated development.The conventional PID algorithm is usually difficult to achieve satisfactory control effect.Ability is not good,but also need to build a precise mathematical model,in addition,PID control parameters can't be timely online correction.For intelligent control,it is in the forefront of the development of automatic control,with advanced and innovative way of thinking,to deal with such a complex and uncertain issues to create a new idea.Intelligent control of the system is intelligent control and PID control to carry out organic combination,is now widely used in the actual production operations,the development prospects are very good.In this paper,we have selected the principles and methods of particle swarm optimization,RBF network tuning,fuzzy adaptive and single neuron adaptive,and combined with PID control algorithm to implement the control of liquid level control system The full range of exploration,so that control to achieve adaptive,self-tuning advantages.In this article,the hardware and software systems in the system are briefly introduced.Then,a variety of PID control algorithms are used to simulate and monitor the hydraulic control system.Its main use to Matlab6.5 to complete the system of simulation analysis and calculation,and for its programming control,the use of the LabWindows platform is used.Particle swarm algorithm is a kind of search strategy by observing the process of predation of bird population,and the algorithm is used to iterate and take the parameters.RBF neural network is used to identify and identify Jacobian data And the fuzzy adaptive adjustment algorithm is used to implement the fuzzy inference theory by using the transformation of fuzzy and error to adjust the PID parameters.The single neuron adaptive PID algorithm is based on the number of online correction weights,And then modify its control.In short,a variety of optimization control methods have a certain optimization strategies and methods,and ultimately to achieve the parameters of the PID to adjust the control;for the future in the relevant areas to carry out a deeper level of research,the article in a variety of PID optimization methods after the completion of immediate control,All will carry out the improvement of the algorithm.For this level control system,the above a variety of intelligent optimization for PID control approach,not only has a strong robustness,but also has a small overshoot,theadvantages of good dynamic performance,in addition to the conventional PID algorithm in the steady state of high accuracy,simple structure,and other advantages,while the PID parameters can play the effect of automatic adjustment.This kind of intelligent control algorithm is applied to the LabWindows software platform for its programming work,so as to finally reach the liquid level control,and the application and the actual system.According to the actual control test results analysis,these intelligent PID control algorithm does have a better control capability,the relevant improved control algorithm,will have a more robust adaptability,and can be applied to more complex process control system.