.png){kind=icon}
.png){kind=icon}
Return
Lyapunov-based adaptive deep system identification for approximate dynamic programming
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2025.112462.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent developments in approximate dynamic programming (ADP) use deep neural network (DNN)-based system identifiers to solve the infinite horizon state regulation problem; however, the DNN weights do not continually adjust for all layers. In this paper, ADP is performed using a Lyapunov-based DNN (Lb-DNN) adaptive identifier that involves online weight updates. Provided the Jacobian of the Lb-DNN satisfies the persistence of excitation condition, the Lb-DNN weights exponentially converge to a residual approximation error, and the corresponding control policy converges to a neighborhood of the optimal policy. Simulation results show that the Lb-DNN yields 49.85% improved root mean squared (RMS) function approximation error in comparison to a baseline ADP DNN result and faster convergence of the RMS regulation error, RMS controller error, and RMS function approximation error.
Keywords:
approximate dynamic programming
deep neural networks
Lyapunov-based adaptive identifier
persistence of excitation
optimal control policy
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W