What question did this study set out to answer?

To establish conditions under which suboptimal model predictive control maintains closed-loop stability for nonlinear systems without requiring exact global optimization.

March 1, 1999IEEE Transactions on Automatic ControlOpen Access

Suboptimal model predictive control (feasibility implies stability)

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To establish conditions under which suboptimal model predictive control maintains closed-loop stability for nonlinear systems without requiring exact global optimization.
Derived theoretical conditions under which suboptimal model predictive control formulations guarantee system stability.
Formulated and analyzed two specific suboptimal model predictive control schemes requiring only an initial feasible solution.
Proved that global and exact optimality is not strictly necessary for stability, provided mild feasibility conditions are satisfied.
Demonstrated that the proposed suboptimal schemes guarantee closed-loop stability while significantly lowering computational requirements.

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Implication

Theoretical analysis demonstrates stability in suboptimal model predictive control for nonlinear systems, suggesting feasible solutions suffice without requiring full global optimality.

Cite This Study

Scokaert et al. (1999) studied this question.

synapsesocial.com/papers/69e45d9b61e1519c6da46ff3 https://doi.org/10.1109/9.751369

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Implication

Theoretical analysis demonstrates stability in suboptimal model predictive control for nonlinear systems, suggesting feasible solutions suffice without requiring full global optimality.

Suboptimal model predictive control (feasibility implies stability)

Key Points

Discussion

Implication

Cite This Study

Suboptimal model predictive control (feasibility implies stability)

Key Points

Discussion

Implication

Cite This Study

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