.png){kind=icon}
Return
Adjustable behavior-guided adaptive dynamic programming for neural learning control
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2025.129986.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, an adjustable behavior-guided adaptive dynamic programming (BGADP) algorithm is designed to solve the optimal regulation problem for discrete-time systems. In conventional adaptive dynamic programming methods, gradient information of system dynamics is necessary for conducting policy improvement. However, these methods face challenges when gradient information cannot be computed or when the system dynamics is non-differentiable. To overcome these limitations, a human-behavior-inspired swarm intelligence approach is used to search for superior policies during the iterative process, eliminating the need for gradient information. Additionally, a relaxation factor is introduced into the value function update to accelerate the convergence speed of the algorithm. The monotonicity and convergence properties of the iterative value function are rigorously analyzed. Finally, the effectiveness and practicality of the adjustable BGADP algorithm are validated through two simulation studies, which are implemented using the actor-critic framework with neural networks.
Keywords:
Adaptive dynamic programming
Brain storm optimization
Convergence rate
Neural networks
Optimal control
Swarm intelligence
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control
PREAI
ShareSaveSelf-Learning Optimal Regulation for Discrete-Time Nonlinear Systems Under Event-Driven Formulation
PREAI
ShareSaveDual Event-Triggered Constrained Control Through Adaptive Critic for Discrete-Time Zero-Sum Games
PREAI
ShareSaveSystem Stability of Learning-Based Linear Optimal Control With General Discounted Value Iteration
PREAI
ShareSaveShareSaveAdaptive optimal control for reference tracking independent of exo-system dynamics.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAIShareSave
View more