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Robust Model Predictive Control for Signal Temporal Logic Synthesis

Farahani, Samira S. and Raman, Vasumathi and Murray, Richard M. (2015) Robust Model Predictive Control for Signal Temporal Logic Synthesis. . (Unpublished)

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Most automated systems operate in uncertain or adversarial conditions, and have to be capable of reliably reacting to changes in the environment. The focus of this paper is on automatically synthesizing reactive controllers for cyber-physical systems subject to signal temporal logic (STL) specifications. We build on recent work that encodes STL specifications as mixed integer linear constraints on the variables of a discrete-time model of the system and environment dynamics. To obtain a reactive controller, we present solutions to the worst-case model predictive control (MPC) problem using a suite of mixed integer linear programming techniques. We demonstrate the comparative effectiveness of several existing worst-case MPC techniques, when applied to the problem of control subject to temporal logic specifications; our empirical results emphasize the need to develop specialized solutions for this domain.

Item Type:Report or Paper (Discussion Paper)
Murray, Richard M.0000-0002-5785-7481
Additional Information:This work is supported in part by Northrop Grumman and by TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.
Funding AgencyGrant Number
Northrop Grumman CorporationUNSPECIFIED
Semiconductor Research CorporationUNSPECIFIED
Microelectronics Advanced Research Corporation (MARCO)UNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Subject Keywords:Hybrid systems, model predictive control, uncertain systems, robust optimization, signal temporal logic
Record Number:CaltechAUTHORS:20200214-151424905
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101314
Deposited By: Tony Diaz
Deposited On:14 Feb 2020 23:19
Last Modified:14 Feb 2020 23:19

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