5 years ago

Switched Offline Multiple Model Predictive Control with Polyhedral Invariant Sets

Switched Offline Multiple Model Predictive Control with Polyhedral Invariant Sets
Xiangyuan Tao, Ning Li, Shaoyuan Li, Dewen Li
This paper presents a switched offline multiple model predictive control procedure for nonlinear processes to ease the online computational burden and reduce the number of submodels. We employ the gap metric to characterize the dynamic difference between linear models and establish a linear model bank to approximate the nonlinear system. Based on the robust MPC algorithm, we develop an offline model predictive controller for each submodel. The polyhedral invariant set is utilized to expand the work scope of each local controller. In the offline part, a series of discrete states are selected, the corresponding feedback gains are precomputed, and associated polyhedral invariant sets are constructed. In the online implementation, the control input is simply calculated by calling the feedback gain according to the current state. A switching rule is then designed to integrate the submodels and guarantee the stability of the whole system. Finally, the corresponding simulation example is presented to validate the efficiency of the presented algorithm.

Publisher URL: http://dx.doi.org/10.1021/acs.iecr.7b01412

DOI: 10.1021/acs.iecr.7b01412

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.