Nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

Nonlinear model predictive controllers (NMPC) can predict the future behavior of the under-controlled system using a nonlinear predictive model. Here, an array of hyper chaotic diagonal recurrent neural network (HCDRNN) was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window. In order to improve the convergence of the parameters of the HCDRNN to improve system's modeling, the extent of chaos is adjusted using a logistic map in the hidden layer. A novel NMPC based on the HCDRNN array (HCDRNN-NMPC) was proposed that the control signal with the help of an improved gradient descent method was obtained. The controller was used to control a continuous stirred tank reactor (CSTR) with hard-nonlinearities and input constraints, in the presence of uncertainties including external disturbance. The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection. Parameter convergence and neglectable prediction error of the neural network (NN), guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

Automated summary

This paper introduces a nonlinear model predictive controller (NMPC) based on hyper chaotic diagonal recurrent neural network (HCDRNN) for modeling and predicting the behavior of under-controlled systems. The proposed method demonstrates superior performance in trajectory tracking and disturbance rejection, with advantages including parameter convergence, negligible prediction error, guaranteed stability, and high tracking performance.