The stationary point of observer-based dynamic LQR is characterized by a pair of symmetric discrete-time Sylvester equations, and the usual separated LQR-plus-minimum-trace-observer design is not optimal.
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A framework reformulates safety-constrained infinite-horizon optimal control as an unconstrained problem on an extended state space using barrier-Lyapunov functions, auxiliary variables, adaptive excitation, and online critic learning for disturbed high-relative-degree systems.
An online algorithm for zero-sum LQ games with unknown dynamics combines model estimation and surrogate selection to achieve regret bounds on policy convergence.
citing papers explorer
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On the Optimization Landscape of Observer-based Dynamic Linear Quadratic Control
The stationary point of observer-based dynamic LQR is characterized by a pair of symmetric discrete-time Sylvester equations, and the usual separated LQR-plus-minimum-trace-observer design is not optimal.
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Synthesizing Safety in Infinite-Horizon Optimal Control for Disturbed High-Relative-Degree Systems via Barrier-Regulating Auxiliary Variables
A framework reformulates safety-constrained infinite-horizon optimal control as an unconstrained problem on an extended state space using barrier-Lyapunov functions, auxiliary variables, adaptive excitation, and online critic learning for disturbed high-relative-degree systems.
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An Online Learning Approach for Two-Player Zero-Sum Linear Quadratic Games
An online algorithm for zero-sum LQ games with unknown dynamics combines model estimation and surrogate selection to achieve regret bounds on policy convergence.