Application of an Off‐Policy Reinforcement Learning Algorithm for H∞${{H}_\infty }$ Control Design of Nonlinear Structural Systems With Completely Unknown Dynamics

ABSTRACT This paper proposes a model‐free and online off‐policy algorithm based on reinforcement learning (RL) for vibration attenuation of earthquake‐excited structures, through designing an optimal H∞${{H}_\infty }$ controller. This design relies on solving a two‐player zero‐sum game theory with a...

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Veröffentlicht in:Earthquake engineering & structural dynamics Jg. 54; H. 4; S. 1210 - 1228
Hauptverfasser: Amirmojahedi, M., Mojoodi, A., Shojaee, Saeed, Hamzehei‐Javaran, Saleh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.04.2025
Schlagworte:
Active damping
Algorithms
Civil engineering
Control systems design
Controllers
Earthquakes
Game theory
H-infinity control
H∞${{H}_\infty }$ control
Machine learning
Neural networks
nonlinear building
Nonlinear control
Nonlinear systems
online reinforcement learning
Policies
State feedback
Strategy
System dynamics
two‐player zero‐sum game
Vibration
Zero sum games
ISSN:0098-8847, 1096-9845
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Zusammenfassung:ABSTRACT This paper proposes a model‐free and online off‐policy algorithm based on reinforcement learning (RL) for vibration attenuation of earthquake‐excited structures, through designing an optimal H∞${{H}_\infty }$ controller. This design relies on solving a two‐player zero‐sum game theory with a Hamilton–Jacobi–Isaacs (HJI) equation, which is extremely difficult, or often impossible, to be solved for the value function and the related optimal controller. The proposed strategy uses an actor‐critic‐disturbance structure to learn the solution of the HJI equation online and forward in time, without requiring any knowledge of the system dynamics. In addition, the control and disturbance policies and value function are approximated by the actor, the disturbance, and the critic neural networks (NNs), respectively. Implementing the policy iteration technique, the NNs’ weights of the proposed model are calculated using the least square (LS) method in each iteration. In the present study, the convergence of the proposed algorithm is investigated through two distinct examples. Furthermore, the performance of this off‐policy RL strategy is studied in reducing the response of a seismically excited nonlinear structure with an active mass damper (AMD) for two cases of state feedback. The simulation results prove the effectiveness of the proposed algorithm in application to civil engineering structures.
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ISSN:0098-8847
1096-9845
DOI:10.1002/eqe.4299