HCDRNN-NMPC: A New Approach to Design Nonlinear Model Predictive Control (NMPC) Based on the Hyper Chaotic Diagonal Recurrent Neural Network (HCDRNN)

Abstract

In industrial applications, Stewart platform control is especially important. Because of the Stewart platform’s inherent delays and high nonlinear behavior, a novel nonlinear model predictive controller (NMPC) and new chaotic neural network model (CNNM) are proposed. Here, a novel NMPC based on hyper chaotic diagonal recurrent neural networks (HCDRNN-NMPC) is proposed, in which, the HCDRNN estimates the future system’s outputs. To improve the convergence of the parameters of the HCDRNN to better the system’s modeling, the extent of chaos is adjusted using a logistic map in the hidden layer. The proposed scheme uses an improved gradient method to solve the optimization problem in NMPC. The proposed control is used to control six degrees of freedom Stewart parallel robot with hard-nonlinearity, input constraints, and in the presence of uncertainties including external disturbance. High prediction performance, parameters convergence, and local minima avoidance of the neural network are guaranteed. Stability and high tracking performance are the most significant advantages of the proposed scheme.