FORMULA integrates MPC with CLFs and neural network CBFs for distributed safe formation control in multi-robot systems.
Adaptive clf-mpc with application to quadrupedal robots
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A backstepping-derived reach-avoid invariant set serves as the terminal constraint in sampled-data MPC, proving recursive feasibility and safe steering to the target for continuous nonlinear systems under input constraints and fast sampling.
citing papers explorer
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FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance
FORMULA integrates MPC with CLFs and neural network CBFs for distributed safe formation control in multi-robot systems.
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Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping
A backstepping-derived reach-avoid invariant set serves as the terminal constraint in sampled-data MPC, proving recursive feasibility and safe steering to the target for continuous nonlinear systems under input constraints and fast sampling.