Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
696935 | Automatica | 2012 | 6 Pages |
Abstract
In this work, we design a Lyapunov-based model predictive controller (LMPC) for nonlinear systems subject to stochastic uncertainty. The LMPC design provides an explicitly characterized region from where stability can be probabilistically obtained. The key idea is to use stochastic Lyapunov-based feedback controllers, with well characterized stabilization in probability, to design constraints in the LMPC that allow the inheritance of the stability properties by the LMPC. The application of the proposed LMPC method is illustrated using a nonlinear chemical process system example.
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Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Maaz Mahmood, Prashant Mhaskar,