TY - JOUR
T1 - Decentralized optimal control of large-scale partially unknown nonlinear mismatched interconnected systems based on dynamic event-triggered control
AU - Su, Hanguang
AU - Luan, Xinyang
AU - Zhang, Huaguang
AU - Liang, Xiaodong
AU - Yang, Jinzhu
AU - Wang, Jiawei
N1 - Funding information: This work was supported by National Natural Science Foundation of China (No. 62373091, 62103087 & 62203311), China Postdoctoral Science Foundation (No. 2021M690567), National Key R&D Program of China under grant 2018YFA0702200, National Natural Science Foundation of China under Grant U22A2055, the Fundamental Research Funds for the Central Universities (No. N2104016 & N2304009), Postdoctoral Research Foundation of Northeastern University, and Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - In this paper, a novel decentralized control method is proposed for nonlinear mismatched large-scale interconnected systems subjected to partially unknown dynamics by designing auxiliary control for each subsystem. It is demonstrated that the control sequence consisting of the optimal control policies of auxiliary control can stabilize the system asymptotically, leading to decentralized control of the large-scale system. An integral reinforcement learning (IRL) method is firstly proposed, replacing the traditional policy iterative algorithm to analyze the optimal control problem of each edge subsystem with partially unknown dynamics. After that, the edge-based dynamic event-triggered control algorithm is proposed based on the static event-triggered control method, and an internal dynamic variable characterized by a first-order filter is defined. A single critic neural network (NN) is then designed to learn the approximate optimal control strategy under the dynamic event-triggered mechanism adaptively. The stability analysis is proposed to demonstrate that the state of the event-based pulse system is ultimately uniformly bounded (UUB) and the Zeno behavior is eliminated successfully. Finally, the effectiveness of the proposed algorithm is verified by two simulation examples to realize decentralized control of mismatched large-scale systems.
AB - In this paper, a novel decentralized control method is proposed for nonlinear mismatched large-scale interconnected systems subjected to partially unknown dynamics by designing auxiliary control for each subsystem. It is demonstrated that the control sequence consisting of the optimal control policies of auxiliary control can stabilize the system asymptotically, leading to decentralized control of the large-scale system. An integral reinforcement learning (IRL) method is firstly proposed, replacing the traditional policy iterative algorithm to analyze the optimal control problem of each edge subsystem with partially unknown dynamics. After that, the edge-based dynamic event-triggered control algorithm is proposed based on the static event-triggered control method, and an internal dynamic variable characterized by a first-order filter is defined. A single critic neural network (NN) is then designed to learn the approximate optimal control strategy under the dynamic event-triggered mechanism adaptively. The stability analysis is proposed to demonstrate that the state of the event-based pulse system is ultimately uniformly bounded (UUB) and the Zeno behavior is eliminated successfully. Finally, the effectiveness of the proposed algorithm is verified by two simulation examples to realize decentralized control of mismatched large-scale systems.
KW - Decentralized control
KW - Edge-based dynamic event-triggered control
KW - Integral reinforcement learning (IRL)
KW - Mismatched large-scale interconnection system
KW - Neural networks (NNs)
UR - http://www.scopus.com/inward/record.url?scp=85179714120&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2023.127013
DO - 10.1016/j.neucom.2023.127013
M3 - Article
SN - 0925-2312
VL - 568
JO - Neurocomputing
JF - Neurocomputing
M1 - 127013
ER -