Continuous stirred tank reactor (CSTR) systems are commonly utilized in chemical and biochemical processes. Nonetheless, they often face the challenges with heat transfer limitations and reactant concentrations, as well as the computational burden arising in finding optimal control policies. This article designs a dynamic event-driven optimal tracking neuro-controller for the constrained CSTR systems in the framework of critic learning. The present dynamic event-driven neuro-controller can overcome input limitations. Meanwhile, compared with the time-driven controller, it decreases the computational load up to 75.23%. These characteristics can maximize efficiency and product quality for the CSTR systems.

It is often challenging to design an optimal tracking controller for the continuous stirred tank reactor (CSTR) system due to its nonlinear nature and physical limitations. This article presents a dynamic event-driven optimal tracking neruo-control scheme for the CSTR system with asymmetric input constraints. Initially, an improved nonquadratic cost function is introduced for the CSTR system to tackle asymmetric control restrictions. Then, a dynamic event-driven mechanism together with the event-driven Hamilton-Jacobi-Bellman equation (ED-HJBE) is proposed. To solve the ED-HJBE, a critic neural network (CNN) is constructed within the critic learning framework. The CNN's weight vector is tuned by combining the gradient descent method and the concurrent learning technique. After that, uniform ultimate boundedness of the tracking error and the CNN's weight estimation error is assured based on the Lyapunov method. Finally, experiment studies are conducted to validate the present optimal tracking neuro-control strategy.