Fuzzy Neural Network Model Predictive Control Based on Dynamic Partial Least Squares Framework

An improved fuzzy neural network model predictive control (MPC) method based on dynamic partial least squares (DPLS) framework is proposed for the control of highly coupled nonlinear systems. Firstly, the PLS framework of dynamic neural network is established by adding neural network into the traditional DPLS framework. Secondly, the internal model of latent variable space is established by using the Dung Beetle optimized fuzzy neural network algorithm, and the parameters of the model are optimized by the improved adaptive LM algorithm to obtain the accurate prediction model. Furthermore, the adaptive learning rate gradient descent algorithm is used to optimize the control quantity of the latent variable loop. Finally, the PH neutralization titration process was used for the experiment. The results show that the fuzzy neural network model predictive control based on dynamic PLS framework has good control performance.