Develops a constrained ADP algorithm for linear systems that guarantees perpetual constraint satisfaction via invariant sets and asymptotic convergence to the optimal LQR policy, with a data-driven implementation.
Semidefinite programming,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Approximate Dynamic Programming For Linear Systems with State and Input Constraints
Develops a constrained ADP algorithm for linear systems that guarantees perpetual constraint satisfaction via invariant sets and asymptotic convergence to the optimal LQR policy, with a data-driven implementation.