Dynamic Decentralized Event-Triggered Tracking Control of Continuous Stirred Tank Reactor Systems

• Published in: IEEE transactions on automation science and engineering Vol. 22; pp. 16632 - 16643
• Main Authors: Yang, Xiong, Meng, Jianling, Wei, Qinglai
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Adaptive dynamic programming (ADP); Chemical reactions; continuous stirred tank reactor (CSTR); Coolants; decentralized tracking control; Dynamic programming; Event detection; event-triggered control; Inductors; Interconnected systems; Nonlinear systems; Optimal control; Regulation; Temperature sensors
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2025.3579230

We develop a dynamic decentralized event-triggered tracking control strategy for cascade interconnected continuous stirred tank reactor (CSTR) systems subject to asymmetric input limits. Initially, we construct auxiliary augmented subsystems related to the cascade interconnected CSTR systems. Then, by introducing modified nonquadratic cost functions for the auxiliary augmented subsystems, we convert the decentralized constrained tracking control problem into an array of unconstrained optimal regulation problems. After that, with the construction of dynamic event-triggering mechanisms, we propose the event-triggered Hamilton-Jacobi-Bellman equations (ET-HJBEs) associated with the transformed optimal regulation problems. To approximately solve the ET-HJBEs, we design critic neural networks (CNNs) in the adaptive dynamic programming framework with the CNNs' weights being updated through the gradient descent method. Furthermore, we use Lyapunov method to prove that the CNNs' weight estimation errors and the tracking error are stable in the sense of uniform ultimate boundedness. Finally, simulation results of the three-reactor cascade interconnected CSTR systems are provided to validate the present dynamic decentralized event-triggered tracking control scheme. Note to Practitioners-Cascade interconnected continuous stirred tank reactor (CSTR) systems widely emerge in chemical industries. They are sensitive to variations in the reactant concentration and the reaction temperature. Therefore, it is necessary to regulate the concentration of reactants and the reaction temperature via designing an online tracking controller for cascade interconnected CSTR systems. However, the heat transfer limitations and the computational cost must be considered for designing such a tracking controller, which leads to big challenges. This article designs an online-learning decentralized tracking controller in a dynamic event-triggering mechanism for the cascade interconnected CSTR systems within the framework of adaptive dynamic programming. The present decentralized dynamic event-triggered tracking controller is not only able to conquer input constraints but distinctly cut down the computational cost in comparison with the static event-triggered tracking controllers designed for cascade interconnected CSTR systems.