Flatness generalized prediction control based on wavelet denoising
Control and Decision Conference（2013）
摘要
The delay of flatness measurement signal exists in low-speed cold rolling process, which influences the performance of closed loop control system. In this paper, the flatness generalized predictive control algorithm is proposed. The nonlinear wavelet denoising method is used to filter the noise of the measure data availably. Then the filtered data are applied to recognize the flatness through the model based on multiple linear regression. The prediction model of symmetrical and asymmetrical coefficients can be obtained. And then receding horizon optimization and online identification are developed to obtain control sequence. The simulation results exhibit the effectiveness of our method.
更多查看译文
关键词
optimisation,low-speed cold rolling process,signal denoising,linear regression,closed loop control system performance,measure data noise filtering,receding horizon optimization,wavelet transforms,regression analysis,symmetrical coefficients,flatness measurement signal delay,online identification,delays,flatness generalized predictive control algorithm,control sequence,nonlinear control systems,wavelet transform denoise,multiple linear regression,cold rolling,flatness coefficients,filtering theory,nonlinear wavelet denoising method,asymmetrical coefficients,closed loop systems,horizon optimization,predictive control,data models,prediction algorithms,predictive models,strips,optimization,force
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要