.png){kind=icon}
.png){kind=icon}
Return
Discrete-Time Self-Learning Parallel Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2020.2995646.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, a new self-learning parallel control method, which is based on adaptive dynamic programming (ADP) technique, is developed for solving the optimal control problem of discrete- time time-varying nonlinear systems. It aims to obtain an approximate optimal control law sequence and simultaneously guarantees the convergence of the value function. Establishing the time-varying artificial system by neural networks in a certain time-horizon, a control-sequence-improvement ADP algorithm is developed to obtain the control law sequence. For the first time, the criteria of the parallel execution are presented, such that the value function is proven to converge to a finite neighborhood of the optimal performance index function. Finally, numerical results and analysis are presented to demonstrate the effectiveness of the parallel control method.
Keywords:
Optimal control
Nonlinear systems
Time-varying systems
Performance analysis
Complex systems
Biological neural networks
ACP
adaptive dynamic programming (ADP)
approximate dynamic programming
nonlinear systems
optimal control
parallel control
reinforcement learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W