Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control

By Prashant Mhaskar, N. El‐Farra, Panagiotis D. Christofides. Proceedings of the 2005, American Control Conference, 2005., 2005

359 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/1d67e124a46a83404ab147be1f279ae87025ec0b

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In Control and Systems Engineering

Citations in the corpus

A tutorial review of economic model predictive control methods (2014, background): “, [115]) for imposing state constraints in LEMPC.”
Economic model predictive control of nonlinear process systems using Lyapunov techniques (2012, neutral): “Vðxðtkþ1ÞÞ VðxðtkÞÞ ew VðxðtÞÞ VðxðtkÞÞ; 8t 2 1⁄2tk; tkþ1Þ (22)”
Distributed model predictive control: A tutorial review and future research directions (2013, positive): “Speciﬁcally, we review the LMPC design proposed in Mhaskar et al. (2005, 2006) which allows for an explicit characterization of the stability region and guarantees controller feasibility and closed-loop stability.”
Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems (2019, positive): “LMPC is known for its explicit characterization of stability properties and guaranteed closed-loop stabilization of nonlinear systems in the presence of state and input constraints.(27) LMPC is a powerful tool that combines the complementary properties of the CLF (stability) and predictive control (”
Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses (2008, background): “troller, is that optimality considerations can be taken explicitly into account (as well as constraints on the inputs and the states [34]) in the computation of the controller within an on-line optimization framework, improving the closed-loop performance”
Robust predictive control of switched systems: Satisfying uncertain schedules subject to state and control constraints (2008, methodology): “Utilizing the robustness of the control law of Equation (7) with respect to discrete implementation, the following result can be derived for the predictive controller of Equations (2)–(6) (see [22] for the proof).”
Modelling and optimal control for a fed-batch fermentation process (2013, methodology): “Unstructured and nonsegregational models with specific rates of cell growth, metabolite production, and substrate consumption have been used to model fed-batch fermentation [10–12].”