Abstract
In the industrial process, the controlled object temperature has the characteristics of great inertia, large lag and non-linearity. It is difficult to acquire its exact mathematical model. The traditional temperature control methods are difficult to achieve the desired control effect. Model predictive control (MPC) has the advantages of easily modeling, good robustness and easy realization of dynamic matrix control (DMC). It can meet the requirements of temperature control in practical engineering. This paper builds a data-driving model. The real-time data are collected by using temperature control module. The relationship of heating voltage and sensor temperature is established by using the System Identification Function of MATLAB. And this identified model is applied to the DMC design of model predictive control. The parameters of P and M in DMC control are tuned by simulation. Finally, the designed controller is applied to the real temperature control process. The controller achieves good control effect. The control results can meet the requirements of fast response and good robustness.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
