Event-Triggered Optimal Control for the Continuous Stirred Tank Reactor System

Continuous stirred tank reactor (CSTR)is essential equipment found in chemical processing industries. Control of the CSTR process has long been a challenging issue due to the high complexity and strong nonlinearity of the chemical process. In this article, a novel event-triggered (ET) adaptive dynamic programming (ADP) optimal control algorithm is developed for the CSTR system. To reduce the computational load and the communication data between the controller and the actuator, a novel event-triggering condition based on the Taylor series expansion is designed for the ET controller. With the novel event-triggering condition, 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 the 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 interevent 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.