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Discrete-Time Self-Learning Parallel Control
- Source :
- IEEE Transactions on Systems, Man, and Cybernetics: Systems. 52:192-204
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- 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.
- Subjects :
- Sequence
Mathematical optimization
Artificial neural network
Computer science
010401 analytical chemistry
02 engineering and technology
Function (mathematics)
Optimal control
01 natural sciences
0104 chemical sciences
Computer Science Applications
Human-Computer Interaction
Dynamic programming
Discrete time and continuous time
Control and Systems Engineering
Bellman equation
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
Software
Subjects
Details
- ISSN :
- 21682232 and 21682216
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Accession number :
- edsair.doi...........377b9c4595ec5d978c6ddc0bfd8d8e8f