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Found 12 papers, 1 papers with code
Physics-Informed Representation and Learning: Control and Risk Quantification
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous driving.
Distributionally Robust Path Integral Control
We consider a continuous-time continuous-space stochastic optimal control problem, where the controller lacks exact knowledge of the underlying diffusion process, relying instead on a finite set of historical disturbance trajectories.
Simulator-Driven Deceptive Control via Path Integral Approach
We study the deception problem in the continuous-state discrete-time stochastic dynamics setting and, using motivations from hypothesis testing theory, formulate a Kullback-Leibler control problem for the synthesis of deceptive policies.
Safety Control of Uncertain MIMO Systems Using Dynamic Output Feedback Barrier Pairs
This paper introduces a novel approach for safety control in uncertain MIMO systems with partial state information.
Encrypted Price-based Market Mechanism for Optimal Load Frequency Control
The global trend of energy deregulation has led to the market mechanism replacing some functionality of load frequency control (LFC).
Barrier Pairs for Safety Control of Uncertain Output Feedback Systems
The barrier function method for safety control typically assumes the availability of full state information.
Upper Bounds for Continuous-Time End-to-End Risks in Stochastic Robot Navigation
We present an analytical method to estimate the continuous-time collision probability of motion plans for autonomous agents with linear controlled Ito dynamics.
Attack Impact Evaluation by Exact Convexification through State Space Augmentation
We address the attack impact evaluation problem for control system security.
Optimal Sensor Gain Control for Minimum-Information Estimation of Continuous-Time Gauss-Markov Processes
We consider the scenario in which a continuous-time Gauss-Markov process is estimated by the Kalman-Bucy filter over a Gaussian channel (sensor) with a variable sensor gain.
Simultaneous Perception-Action Design via Invariant Finite Belief Sets
Although perception is an increasingly dominant portion of the overall computational cost for autonomous systems, only a fraction of the information perceived is likely to be relevant to the current task.
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes
The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation.
Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory
We first establish a novel connection between a closed-loop identification problem of interest and a channel coding problem involving an additive white Gaussian noise (AWGN) channel with feedback and a certain structural constraint.
