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Advanced Control of Overhead Crane

Author: YangChunYan
Tutor: ChuJiZheng
School: Beijing University of Chemical Technology
Course: Control Science and Engineering
Keywords: Bridge crane Trolley hoist system Positioning and anti-sway control RBF neural network control Simulation
CLC: TH215
Type: Master's thesis
Year: 2011
Downloads: 80
Quote: 0
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Abstract


Bridge crane as one of the modern logistics equipment , widely used in various industrial applications , to eliminate or control the swing hoists great significance to improve the efficiency and security of the Cranes . Hoists anti-sway control technology is the crane as modern logistics and equipment necessary to have one of the functions , the dynamic analysis of the bridge crane system is to solve the crane quickly bit and hoists the basis of the anti- away problem . Firstly Lagrangian method to derive the kinetic equation - three-dimensional , two-dimensional and one-dimensional mathematical model of the bridge crane system of the overhead crane system with universal significance . Nonlinear dynamic equations of the crane system can be simplified , in the reasonable range of linear kinetic equations of the bridge crane system - three-dimensional , two-dimensional and one-dimensional linear model of the bridge crane system for the study of bridge anti-sway crane provides a theoretical basis . The difficulty and cost of measuring site for hoists swing angle variables , the use of the car location information to design a full - state observer . Relevant state variables space by setting the state observer reconstruction , which will include car position , including estimates of the state variables available to the anti-sway control system . Papers pole placement state feedback control method , linear quadratic regulator (LQR) optimal control and proportional-integral- derivative (PID) control method , crane anti-sway simulation . Simulation results show that the modern control methods have some limitations . Based on the analysis of neural network theory , radial basis function (RBF) neural network adaptive PID control algorithm applied to the bridge crane heavy anti-sway system . Two RBF neural network adaptive PID controller to control the car 's position and load swing . Adaptive learning ability of the neural network , the online tuning PID controller proportion ( P ) , integral (I) and derivative (D) three internal parameters , PID control with the best combination of parameters . Simulation results show that the algorithm is no crane positioning static error , no overshoot , while the rapid elimination of the swing of the load .

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CLC: > Industrial Technology > Machinery and Instrument Industry > Lifting machinery and transport machinery > Lifting machinery > Generally with overhead crane
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