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20132023
most citedTime-invariant Prefix Coding for LQG Control

8 citations · 33 across the 28 of their papers we have counts for

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12 papers · 1 filter

eess.SY2023

Simulator-Driven Deceptive Control via Path Integral Approach

Apurva Patil, Mustafa O. Karabag, Takashi Tanaka +1

We consider a setting where a supervisor delegates an agent to perform a certain control task, while the agent is incentivized to deviate from the given policy to achieve its own g…

eess.SY2023

Safety Control of Uncertain MIMO Systems Using Dynamic Output Feedback Barrier Pairs

Binghan He, Takashi Tanaka

Safety control of dynamical systems using barrier functions relies on knowing the full state information. This paper introduces a novel approach for safety control in uncertain MIM…

eess.SY2022

Barrier Pairs for Safety Control of Uncertain Output Feedback Systems

Binghan He, Takashi Tanaka

The barrier function method for safety control typically assumes the availability of full state information. Unfortunately, in many scenarios involving uncertain dynamical systems,…

eess.SY2022

Upper Bounds for Continuous-Time End-to-End Risks in Stochastic Robot Navigation

Apurva Patil, Takashi Tanaka

We present an analytical method to estimate the continuous-time collision probability of motion plans for autonomous agents with linear controlled Ito dynamics. Motion plans genera…

eess.SY2022

Attack Impact Evaluation by Exact Convexification through State Space Augmentation

Hampei Sasahara, Takashi Tanaka, Henrik Sandberg

We address the attack impact evaluation problem for control system security. We formulate the problem as a Markov decision process with a temporally joint chance constraint that fo…

eess.SY2021

Optimal Sensor Gain Control for Minimum-Information Estimation of Continuous-Time Gauss-Markov Processes

Vrushabh Zinage, Takashi Tanaka, Valeri Ugrinovskii

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. The pr…