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Found 6 papers, 1 papers with code
Convolutional Bayesian Filtering
In this paper, we find that by adding an additional event that stipulates an inequality condition, we can transform the conditional probability into a special integration that is analogous to convolution.
Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
We prove that the local convergence rates for IntRL using the trapezoidal rule and Bayesian quadrature with a Mat\'ern kernel to be $O(N^{-2})$ and $O(N^{-b})$, where $N$ is the number of evenly-spaced samples and $b$ is the Mat\'ern kernel's smoothness parameter.
Robust Bayesian Inference for Moving Horizon Estimation
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers.
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator
At the core of our results is the uniqueness of the stationary point of dLQR when it is observable, which is in a concise form of an observer-based controller with the optimal similarity transformation.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error.
