Extremal Dynamics Based MEMETIC Algorithm And Its Applications In Nonlinear Predictive Control

As oneofthe mostpopular advancedprocesscontrol (APC) solutions,the model predictive control (MPC) has been widely applied in processindustries during recent years. Due to the rapid development of industryand technology, the control performance requirements for the large-scale,complicated and uncertain system keep rising. Basically, the issuesmentioned above mainly involve solving various types of complicatednonlinear optimization problems. The linear model predictive control(LMPC), which usually relies on a linear dynamic model, is inadequateforabovementionedproblemsandthelimitationbecomesmoreandmoreobvious. Nonlinear model predictive control (NMPC) appears to be aperspective method. However, the introduction of nonlinear predictivemodel brings some difficulties, such as the parameters/structure selectionof the nonlinear predictive model and the online receding horizonoptimization. All these issues encourage researchers and practitioners todevote themselves to the development of novel optimization methodswhich are suitable for the applications in nonlinear model predictivecontrol.The evolutionary algorithms (EAs) have been widely used to solvemanycomplex nonlinear problems in control engineering,suchas systemanalysis, model identification and controller design, and shown theadvantages of global search efficiency, generality and practicality.However, due to the inherited shortcomings of Darwin's theory, theevolution of computations often suffer from low local search capabilityand accuracy. In order to overcome these limitations,"MemeticAlgorithm"(MA) was proposed by incorporating the modern evolutiontheory and Dawkins's"Meme"concept into evoluation computation. MAis a very efficient stochastic heuristics for global optimization, bycombining the global search nature of EA with local search to improve individualsolutions.Thisthesisstartswiththegeneralreviewoftwokeyresearchissuesinnonlinear model predictive control: prediction model andrecedinghorizonoptimization. Base on the newly proposed concept called"Extremal dynamics"in statistical physics and co-evolution theory, weproposed two highly efficient optimization algorithms under theframework of MA, and then applied them to a number of benchmarkproblems, such as numerical optimizations, NMPC online optimizaitionand industrial applications. The main topics studied in this thesis aresummarizedasfollows:(1)Regarding the disadvantages of current evolutionary computationin theoretical foundation and local search effectiveness, theinter-relations between co-evolution, memetic algorithm andextremal dynamics are studied. Anovel hybrid memetic algorithmwith the integration of"Extremal Optimization"and"Levenberg–Marquardt"is proposed, for unconstrained nonlinearoptimization. The proposed method is then employed for trainingof multilayer perceptron (MLP) networks and its effectiveness isdemonstrated.(2)Considering the existence of constraints in optimal controlproblems, a novel EO-SQP algorithm is designed for theoptimization of nonlinear problems with/without constraints. Thesearch dynamics of the proposed methods are analysed in detail.The effectiveness and the efficiency of the proposed methods areproven by the comparison analysis on a number of benchmarknumericalNLPproblems.(3)For the reqirements of model identification and online recedinghorizon optimization in NMPC, the extremal optimization isextendedandfurtherappliedtothesimultaneouslyoptimizationofSVR kernel functions/related parameters and the building ofdymamical model according to multi-step-ahead error. Then,based on the predictive model, a"Horizon based mutation"EO-SQP is designed and served as the online optimizer of aNMPC controller. Finally the simulation on a nonlinear MIMO CSTR is carried out to show the performance of the proposedalgorithm.(4)For a typical nonlinear industrial process - basic oxygen furnaces(BOF) in steelmaking, the proposed neural network model basedon EO-LM learning algorithm is further applied to predict theend-pointproductquality;andtheEO-SQPalgorithmisemployedto find the optimal recipe adjustment. Then an integrated BOFproduction quality control system based on the above mentionedprediction model and optimal recipe adjustment algorithm isdeveloped. The effectiveness of the proposed algorithm is provenbythesimulationresultsonpracticalproductiondata.