Study On Nonlinear Model And Predictive Control Based On Intelligent Algorithm

The life science and the computer technique have developed rapidly,and massively parallel processing technology(MPP) has been presented.Complexity research presented is a new interdisciplinary science,which takes the uncertainty,nonlinearity,time irreversibility as connotations and takes complex problems as objects.Especially,the simulating biology intelligent techniques have been introduced gradually,such as Particle Swarm Optimization(PSO),Genetic Algorithm(GA) and Artificial Neural Network (ANN).The algorithms have random search performance.The techniques provide a new research way in solving problems for the civil engineering of time variant characteristics. Moreover the important achievements have been obtained in the scientific research.Based on the above background,combining development achievements of the intelligent study,Variation PSO(VPSO) is introduced into the back-analysis according to engineering complexity and time variation in this dissertation.A new algorithm is presented,VPSO combines Elman feedback network(ENN) and forms VPSO-ENN.The method is employed in the intelligent analysis of geotechnical engineering.It is successfully realized in the nonlinear parameter identification and deformation predictive control.The dissertation includes the following contents:Firstly,the biological operation mechanism of the standard PSO(SPSO) has been systematically studied.SPSO is easy to get locked into the local optimum value and premature convergence,when the nonlinear optimization problems of the high-dimension and multi-peak are solved.The random variation strategy of particle velocity is adopted to improve SPSO in this dissertation.VPSO,which has rapid global convergence,is presented.According to optimize five Benchmark testing functions of the high-dimension and complexity,the results show that VPSO has simple search mechanism,few adjusting parameters and no gradient information.Compared with SPSO,the VPSO performance is significantly improved in convergence precision, convergence speed and computational stability.Secondly,a new algorithm is presented by combining VPSO algorithm with the ENN.VPSO is used to optimize and determine ENN structure,which does not depend on the gradient information.Using VPSO to search a group of the optimal weights and thresholds,the global optimal solution can effectively be found.The ENN defects of the low learning convergence speed and the easily appearing local minimum has been overcome.Moreover,the method can improve network training speed,increase nonlinear mapping and generalization ability,and approach arbitrary nonlinear functions.Thirdly,the identification model of nonlinear parameters is constructed based on the VPSO-ENN,the implicit mathematical expressions are established.Optimizing the implicit objective function,the high precision fitting is realized between expected output value of the actual system and output value of the identification model.The goal of the nonlinear parameter identification is achieved.The results show that the VPSO-ENN identification model has strong identification ability,simple operation and high identification accuracy.It is feasible to identify unknown parameters of geotechnical engineering.Fourthly,according to the bionic optimization principle of VPSO algorithm and networked control theory,intelligent predictive control has been used to establish VPSO-ENN system,which is one of the multi-input/multi-outinput(MIMO) model.The first derivative and the second derivatives,which are of the past output values in system, are added to the input of the prediction model(PM).Then VPSO-ENN prediction model, called VPSO-ENNPM,has dynamic feedback characteristics.The control law of the prediction system can be obtained based on the optimized objective function of prediction information.The increases of iterative error of recursive prediction model can been avoided effectively.The intelligent predictive control of time-varying system is realized.Finally,using VPSO-ENN control technology and applying time-scroll technology, a set of VPSO-ENN multi-step prediction control system has been established.The syetem integrates foundation pit deformation prediction and control together.The nonlinear implicit equations are constructed between the target output and prediction output.This approach can be avoided the complicated structure relation and mechanical calculation in geotechnical engineering successfully.The program has been compiled with MATIAB7.0.Using the limited history and the latest observation data,the multi-step prediction of foundation pit construction is successfully realized.The results of engineering cases indicate that intelligent prediction method based on the VPSO-ENN has high prediction accuracy and strong generalization ability.It is suitable for the prediction intelligent control of future tendency.The large construction process of civil engineering can be controlled in real-time.