Event-Triggered Optimal Control for Continuous Stirred Tank Reactor System

Abstract

Continuous stirred tank reactor (CSTR) is essential equipment found in chemical processing industries. Control of CSTR process has long been a challenging issue due to the high complexity and strong nonlinearity of the chemical process. In this paper, a novel event-triggered (ET) adaptive dynamic programming (ADP) optimal control (OC) algorithm is developed for continuous stirred tank reactor (CSTR) system. In order to reduce the computational load and the communication data between the controller and the actuator, a new event-triggering condition based on Taylor series expansion is designed for the ET controller. The control policy does not update unless the event-triggering condition is not satisfied. Compared with the time-triggering mechanism, the update frequency of the controller can be obviously reduced. Then, an identifer-actor-critic structure is developed to implement the ET ADP controller. In particular, to overcome the challenge of establishing an exact dynamic for CSTR system, an identifier neural network (NN) is employed to reconstruct the unknown system dynamic based on offline data. Furthermore, the actor-critic structure is developed to obtain the ET control law and the value function. In actor and critic NNs, weights are turned just at the triggered instant and remained constant during the inter event times. Finally, the developed ET ADP controller is applied to the CSTR system. Experimental results show that the developed ET approach can cut down the update frequency to 66\\%, which is very significant for the real CSTR system.