A trajectory-based method using convex feasible points computes maximal controlled invariant sets and enables recursively feasible MPC without terminal sets for linear discrete-time systems.
Constrained model predictive control: Stability and optimality,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
FAIL iteratively learns maximal state-control invariant sets from one-step failing state-input pairs for deterministic LTI systems with polytopic constraints, proving monotonic convergence to the true MSCI without dynamics knowledge.
A finite-horizon NMPC framework with a smooth point-to-cloud distance metric and control barrier functions achieves accurate set-point tracking and smooth obstacle avoidance for aerial robots.
citing papers explorer
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A Trajectory-Based Approach to Controlled Invariance and Recursively Feasible MPC
A trajectory-based method using convex feasible points computes maximal controlled invariant sets and enables recursively feasible MPC without terminal sets for linear discrete-time systems.
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Failure-Aware Iterative Learning of State-Control Invariant Sets
FAIL iteratively learns maximal state-control invariant sets from one-step failing state-input pairs for deterministic LTI systems with polytopic constraints, proving monotonic convergence to the true MSCI without dynamics knowledge.
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Point-to-Cloud NMPC with Smooth Avoidance Constraints
A finite-horizon NMPC framework with a smooth point-to-cloud distance metric and control barrier functions achieves accurate set-point tracking and smooth obstacle avoidance for aerial robots.