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

Xinyang Luan, Hanguang Su*, Huaguang Zhang, Xiaodong Liang, Yuling Liang, Jiawei Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number127042
Early online date18 Nov 2023
Publication statusE-pub ahead of print - 18 Nov 2023

Cite this