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Found 8 papers, 5 papers with code
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design.
Policy Iteration for Multiplicative Noise Output Feedback Control
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem.
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity.
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
An algorithm based on the least-squares method and multiple-trajectory data is proposed for joint estimation of the nominal system matrices and the covariance matrix of the multiplicative noise.
Approximate Midpoint Policy Iteration for Linear Quadratic Control
We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings.
Robust Learning-Based Control via Bootstrapped Multiplicative Noise
Despite decades of research and recent progress in adaptive control and reinforcement learning, there remains a fundamental lack of understanding in designing controllers that provide robustness to inherent non-asymptotic uncertainties arising from models estimated with finite, noisy data.
Sparse optimal control of networks with multiplicative noise via policy gradient
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks.
Learning robust control for LQR systems with multiplicative noise via policy gradient
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces.
