Augments MPC with a safety value function terminal constraint to achieve recursive feasibility and persistent safety while co-optimizing performance.
Iterative convex optimization for model predictive control with discrete-time high-order control barrier functions
4 Pith papers cite this work. Polarity classification is still indexing.
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A geometric characterization of the tangent cone to feasible points in MPECs yields stationarity concepts and constraint qualifications that avoid the strong nondegeneracy and smoothness assumptions required by classical nonlinear programming approaches.
Exact penalization for MPECs is enabled under broader conditions by fractional-order penalties derived from Lojasiewicz error bounds on KKT residual mappings.
An MPEC is an optimization problem whose feasible set is partly defined by another optimization, variational inequality, complementarity system, or equilibrium model.
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Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Augments MPC with a safety value function terminal constraint to achieve recursive feasibility and persistent safety while co-optimizing performance.
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First-Order Optimality Conditions for Mathematical Programming with Equilibrium Constraints
A geometric characterization of the tangent cone to feasible points in MPECs yields stationarity concepts and constraint qualifications that avoid the strong nondegeneracy and smoothness assumptions required by classical nonlinear programming approaches.
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Introduction to Exact Penalization for Mathematical Programming with Equilibrium Constraints
Exact penalization for MPECs is enabled under broader conditions by fractional-order penalties derived from Lojasiewicz error bounds on KKT residual mappings.
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Introduction to Mathematical Programming with Equilibrium Constraints (MPECs) and Bilevel Optimization
An MPEC is an optimization problem whose feasible set is partly defined by another optimization, variational inequality, complementarity system, or equilibrium model.