Lyapunov-based model predictive control of stochastic nonlinear systems

Authors:
Maaz Mahmood;Prashant Mhaskar
Affiliations:
-;-
Venue:
Automatica (Journal of IFAC)
Year:
2012

Citing 3
Cited 1

Brief paper: Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality

Automatica (Journal of IFAC)
Convexity and convex approximations of discrete-time stochastic control problems with constraints

Automatica (Journal of IFAC)

Stochastic model predictive control of LPV systems via scenario optimization

Automatica (Journal of IFAC)

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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.