CAJ | 학술논문

广义预测控制(Generalized Predictive Control,GPC)汲取了DMC(Dynamic Matrix Control)、MAC(Model AlgorithmicControl)中的多步预测优化策略,抗负载扰动、随机噪声、时延变化等能力强,且选取模型参数少,利于控制.然而,据研究发现GPC对模型失配问题有一定的局限性.最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)是在支持向量机的研究基础上发展而来的,具有良好的回归、分类功能.在认真学习LS-SVM原理的基础上,提出了基于LS-SVM误差补偿的广义预测控制,并选择两个模型进行了仿真实验.通过与常规GPC的比较,表明了该算法具有更优的控制性能.
엄의예측공제(Generalized Predictive Control,GPC)급취료DMC(Dynamic Matrix Control)、MAC(Model AlgorithmicControl)중적다보예측우화책략,항부재우동、수궤조성、시연변화등능력강,차선취모형삼수소,리우공제.연이,거연구발현GPC대모형실배문제유일정적국한성.최소이승지지향량궤(Least Squares Support Vector Machine,LS-SVM)시재지지향량궤적연구기출상발전이래적,구유량호적회귀、분류공능.재인진학습LS-SVM원리적기출상,제출료기우LS-SVM오차보상적엄의예측공제,병선택량개모형진행료방진실험.통과여상규GPC적비교,표명료해산법구유경우적공제성능.
Learning the multi-step forecast optimization strategy from Dynamic Matrix Control(DMC) and Model Algorithmic Control(MAC),Generalized Predictive Control(GPC) has a strong ability to overcome load disturbance,random noise and delay change,and the selected model has less parameters,so it is easy to control.However, according to rezearch,GPC has some limitations in the problem of model mismatch.LS-SVM is developed based on Support Vector Machines,and has sound functions in regression and classification.On the basis of conscientiously studying the Least Squares Support Vector Machine(LS-SVM) principle,the GPC based on LS-SVM error compensation is proposed,and is simulated on two models.From the comparison with the conventional GPC,it proves that the algorithm had better performances in control.