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Optimization For PID Control Method On Hydraulic Servo Control System

Posted on:2010-11-15Degree:MasterType:Thesis
Country:ChinaCandidate:J G ZhouFull Text:PDF
GTID:2132360278974928Subject:Light Industry and Chemical Technology and Engineering
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Hydralic servo control system is an automatical control system based on the hydraulic transmission and automatical control theories. It is called by its good dynamic performance and is widely applicated in the industry practices.In the actual hydralic servo control system , it is widely adopted PID controller to improve its performance. The traditional PID parameter tuning way is not fit for the system' s character s of nonlinearity , time-delay, time-variance ac.So, it needs using more effective control strategy for hydralic servo.In recent years, a series of new algorithm such as the Neural Network, Simulated Annealing, Evolutionary Algorithm, Genetic Algorithm and Immunity Algorithm, etc. puts forward one after another, they have appeared much valid in the large-scale and complicated system of combine explode problem, and have large advantages in general usage, robustness, simplification , parallel process etc.. They have applied new thought ang method for resolving complicated question and have been appreciated by researcher , and already been researched in application of hydrali servo control system.In this dissertation the theories of GA, Quantum-behaved Particle Swarm Optimization(QPSO) algorithm are researched and their astringencies have been analysed . It is been known that QPSO is a new evolutive optimized algorithm compared by the example of PID parameter tuning, It has preferable global convergence and robust without any object' s characteristic information , it' s a more efficient parallel search algorithm, and very fit for acquiring an optimized answering to the complicated questionFacing to characters that the hydraulic servo control system' s controled process mechanism is more complicated, and has high nonlinear , time-variable, etc. its process parameter, even model structure mayebe change under the influence of noise, interference of load, .This article used the PID mulriple optimized controlstrategy combining adapting control with QPSO to online adjust the PID parameter in order to fit the request of real-time control .
Keywords/Search Tags:hydralic servo control, PIDcontrol, Genetic Algorithm, Quantum-behaved Particle Swarm Optimization, adaptive control
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