哈尔滨工业大学学报（英文版）2008，Vol.15Issue(6)：763-766,4.
Neuro-fuzzy predictive control for nonlinear application
Neuro-fuzzy predictive control for nonlinear application
摘要
Abstract
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.关键词
model predictive control/fuzzy neural network/nonlinear optimization/adaptive controlKey words
model predictive control/fuzzy neural network/nonlinear optimization/adaptive control分类
信息技术与安全科学引用本文复制引用
CHEN Dong-xiang,WANG Gang,LV Shi-xia..Neuro-fuzzy predictive control for nonlinear application[J].哈尔滨工业大学学报（英文版）,2008,15(6):763-766,4.基金项目
Sponsored by the National Electric Power Corporation Foundation of China(Grant No. SPKJ010-27). （Grant No. SPKJ010-27）
