A Single-Neuron-Based Temperature Control of a Continuous Stirred Tank Reactor

Abstract

In this paper, a new technique to determine the best values of a PID controller is presented. The proposed scheme is based on using a single-neuron controller which its weights represent the PID parameters. Weight’s adjustment is accomplished with a recent meta-heuristic algorithm called the DragonFly Algorithm. To show the effectiveness of our method, we have applied it to control a Continuous Stirred Tank Reactor. The obtained results are compared with several algorithms: the Ziegler–Nichols, Genetic Algorithm, and Particle Swarm Optimization.