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Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach

Authors:

Shakiba Yaghoubi,

Georgios FainekosAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 18, Issue 5s

Article No.: 107, Pages 1 - 20

https://doi.org/10.1145/3358239

Published: 08 October 2019 Publication History

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Abstract

In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.

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Cited By

Hashemi NWilliams SHoxha BProkhorov DFainekos GDeshmukh J(2024)LB4TL: A Smooth Semantics for Temporal Logic to Train Neural Feedback ControllersIFAC-PapersOnLine10.1016/j.ifacol.2024.07.44558:11(183-188)Online publication date: 2024
https://doi.org/10.1016/j.ifacol.2024.07.445
Hashemi NQin XDeshmukh JFainekos GHoxha BProkhorov DYamaguchi T(2023)Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives2023 American Control Conference (ACC)10.23919/ACC55779.2023.10156345(4096-4103)Online publication date: 31-May-2023
https://doi.org/10.23919/ACC55779.2023.10156345
Charitidou MDimarogonas D(2023)Receding Horizon Control With Online Barrier Function Design Under Signal Temporal Logic SpecificationsIEEE Transactions on Automatic Control10.1109/TAC.2022.319547068:6(3545-3556)Online publication date: Jun-2023
https://doi.org/10.1109/TAC.2022.3195470
Show More Cited By

Index Terms

Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic control
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Adversarial learning

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 18, Issue 5s

Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019

October 2019

1423 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3365919

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 08 October 2019

Accepted: 01 July 2019

Revised: 01 June 2019

Received: 01 April 2019

Published in TECS Volume 18, Issue 5s

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Cited By

Hashemi NWilliams SHoxha BProkhorov DFainekos GDeshmukh J(2024)LB4TL: A Smooth Semantics for Temporal Logic to Train Neural Feedback ControllersIFAC-PapersOnLine10.1016/j.ifacol.2024.07.44558:11(183-188)Online publication date: 2024
https://doi.org/10.1016/j.ifacol.2024.07.445
Hashemi NQin XDeshmukh JFainekos GHoxha BProkhorov DYamaguchi T(2023)Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives2023 American Control Conference (ACC)10.23919/ACC55779.2023.10156345(4096-4103)Online publication date: 31-May-2023
https://doi.org/10.23919/ACC55779.2023.10156345
Charitidou MDimarogonas D(2023)Receding Horizon Control With Online Barrier Function Design Under Signal Temporal Logic SpecificationsIEEE Transactions on Automatic Control10.1109/TAC.2022.319547068:6(3545-3556)Online publication date: Jun-2023
https://doi.org/10.1109/TAC.2022.3195470
Dang TDonzé AHaque IKekatos NSaha I(2023)Counter-Example Guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications2023 62nd IEEE Conference on Decision and Control (CDC)10.1109/CDC49753.2023.10383831(5339-5344)Online publication date: 13-Dec-2023
https://doi.org/10.1109/CDC49753.2023.10383831
Kaur RRoul RBatra S(2023)Multilayer extreme learning machine: a systematic reviewMultimedia Tools and Applications10.1007/s11042-023-14634-482:26(40269-40307)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11042-023-14634-4
Leung KPavone M(2022)Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications2022 American Control Conference (ACC)10.23919/ACC53348.2022.9867345(178-185)Online publication date: 8-Jun-2022
https://doi.org/10.23919/ACC53348.2022.9867345
Leung KAréchiga NPavone M(2022)Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methodsThe International Journal of Robotics Research10.1177/0278364922108211542:6(356-370)Online publication date: 28-May-2022
https://doi.org/10.1177/02783649221082115
Hashimoto WHashimoto KTakai S(2022)STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural NetworksIEEE Robotics and Automation Letters10.1109/LRA.2022.31551977:2(5246-5253)Online publication date: Apr-2022
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Liu WMehdipour NBelta C(2022)Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety ConstraintsIEEE Control Systems Letters10.1109/LCSYS.2021.30499176(91-96)Online publication date: 2022
https://doi.org/10.1109/LCSYS.2021.3049917
Verdier CKochdumper NAlthoff MMazo M(2022)Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specificationsAutomatica (Journal of IFAC)10.1016/j.automatica.2022.110184139:COnline publication date: 16-May-2022
https://dl.acm.org/doi/10.1016/j.automatica.2022.110184
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