Lyapunov-based model predictive control of stochastic nonlinear systems

• Published in: Automatica (Oxford) Vol. 48; no. 9; pp. 2271 - 2276
• Main Authors: Mahmood, Maaz, Mhaskar, Prashant
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2012; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Control systems; Control theory. Systems; Design engineering; Dynamical systems; Exact sciences and technology; Mathematical models; Nonlinear dynamics; Optimal control; Predictive control; Stability; Stabilization; Stochastic systems; Stochasticity; System theory; Stochastic systems; Predictive control; Probabilistic approach; Stabilization; Inheritance; Non linear control; Feedback regulation; Control synthesis; Stability region; Non linear system; Stochastic system; Model predictive control; Chemical reactor; Uncertain system; Model reference adaptive control systems; Stochastic control; Non deterministic system; Lyapunov function
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2012.06.033

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.