Abstract
Most actual industrial processes are multivariable and constrained complex systems. The state and output of the system also have uncertainty due to the existence of random disturbances. The output of the system is easier to be measured than the state and can more intuitively reflect the running state of the system. Considering the limitations of industrial equipment and the benefits of production, it is generally allowed to have a small portion of the output that can exceed the constraints. As a result, the outputs can be represented as corresponding single probabilistic constraints. In this paper, therefore, an output probabilistic constrained optimal control algorithm based on multivariable model algorithm control (MMAC) is proposed. First, the feedback correction link of the MMAC algorithm is improved, and the predicted outputs are corrected by taking the weighted average of the errors. Then, assuming that the disturbances obey Gaussian distribution, the output probabilistic constraints are transformed into deterministic ones. Next, the optimal control problem is solved as a quadratic programming (QP) problem after combining them with the performance index function of the MMAC. Finally, the proposed algorithm is applied to the looper control system in hot strip rolling process and compared with the single MMAC algorithm to verify its effectiveness.
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.
