TY - JOUR

T1 - Dynamic event-triggered-based online IRL algorithm for the decentralized control of the input and state constrained large-scale unmatched interconnected system

AU - Luan, Xinyang

AU - Su, Hanguang

AU - Zhang, Huaguang

AU - Liang, Xiaodong

AU - Liang, Yuling

AU - Wang, Jiawei

N1 - Funding information: This work was supported by National Natural Science Foundation of China (62103087), China Postdoctoral Science Foundation (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 (N2104016), Postdoctoral Research Foundation of Northeastern University, and Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province.

PY - 2023/11/18

Y1 - 2023/11/18

N2 - This article proposed a novel adaptive decentralized control (ADC) method for the continuous-time state-constrained and input-constrained large-scale unmatched interconnection system by the means of the adaptive critic design in the dynamic event-triggered (DET) mechanism. The barrier function is used to transform the state-constrained system to the common system without constrained states. To overcome the influence of the unmatched interconnection terms, the auxiliary systems are devised for each subsystem of the large-scale system. Moreover, the non-quadratic utility functions are introduced to constrain the input of the auxiliary systems. The decentralized control scheme of the large-scale unmatched interconnection system can be realized by solving the optimal control schemes of the auxiliary systems. Then with the help of the single critic neural network (NN), the approximate optimal control policies are acquired by designing the approximate cost functions. After that, an integral reinforcement learning (IRL)-based scheme is proposed to solve the integral Bellman equation rather than the complex coupled Hamilton–Jacobi-Bellman equation (HJBE). Simultaneously, the DET mechanism is introduced to reduce the computation efforts and communication loads. The asymptotic stability of the constrained large-scale unmatched interconnection system is proved. In addition, the infamous Zeno behaviour is effectively avoided. Finally, two simulation cases are given to verify the effectiveness of the proposed algorithm.

AB - This article proposed a novel adaptive decentralized control (ADC) method for the continuous-time state-constrained and input-constrained large-scale unmatched interconnection system by the means of the adaptive critic design in the dynamic event-triggered (DET) mechanism. The barrier function is used to transform the state-constrained system to the common system without constrained states. To overcome the influence of the unmatched interconnection terms, the auxiliary systems are devised for each subsystem of the large-scale system. Moreover, the non-quadratic utility functions are introduced to constrain the input of the auxiliary systems. The decentralized control scheme of the large-scale unmatched interconnection system can be realized by solving the optimal control schemes of the auxiliary systems. Then with the help of the single critic neural network (NN), the approximate optimal control policies are acquired by designing the approximate cost functions. After that, an integral reinforcement learning (IRL)-based scheme is proposed to solve the integral Bellman equation rather than the complex coupled Hamilton–Jacobi-Bellman equation (HJBE). Simultaneously, the DET mechanism is introduced to reduce the computation efforts and communication loads. The asymptotic stability of the constrained large-scale unmatched interconnection system is proved. In addition, the infamous Zeno behaviour is effectively avoided. Finally, two simulation cases are given to verify the effectiveness of the proposed algorithm.

KW - Dynamic event-triggered (DET) mechanism

KW - Adaptive decentralized control (ADC)

KW - Constrained states and inputs

KW - Unmatched large-scale interconnection system

KW - Integral reinforcement learning (IRL)

KW - Neural network (NN)

U2 - 10.1016/j.neucom.2023.127042

DO - 10.1016/j.neucom.2023.127042

M3 - Article

SN - 0925-2312

JO - Neurocomputing

JF - Neurocomputing

M1 - 127042

ER -