
Scalable Synthesis of MinimumInformation LinearGaussian Control by Distributed Optimization
We consider a discretetime linearquadratic Gaussian control problem in...
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The Parallelization of Riccati Recursion
A method is presented for parallelizing the computation of solutions to ...
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Rationally Inattentive PathPlanning via RRT*
We consider a pathplanning scenario for a mobile robot traveling in a c...
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Superconvergence of Online Optimization for Model Predictive Control
We develop a oneNewtonstepperhorizon, online, lagL, model predictiv...
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Algorithms for Optimal Control with FixedRate Feedback
We consider a discretetime linear quadratic Gaussian networked control ...
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Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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LQG Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Scalable Synthesis of MinimumInformation LinearGaussianControl by Distributed Optimization
We consider a discretetime linearquadratic Gaussian control problem in which we minimize a weighted sum of the directed information from the state of the system to the control input and the control cost. The optimal control and sensing policies can be synthesized jointly by solving a semidefinite programming problem. However, the existing solutions typically scale cubic with the horizon length. We leverage the structure in the problem to develop a distributed algorithm that decomposes the synthesis problem into a set of smaller problems, one for each time step. We prove that the algorithm runs in time linear in the horizon length. As an application of the algorithm, we consider a pathplanning problem in a state space with obstacles under the presence of stochastic disturbances. The algorithm computes a locally optimal solution that jointly minimizes the perception and control cost while ensuring the safety of the path. The numerical examples show that the algorithm can scale to thousands of horizon length and compute locally optimal solutions.
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