Implementation of Neural Network for Generalized Predictive Control: A Comparison between a Newton Raphson and Levenberg Marquardt Implementation

An efficient implementation of generalized predictive control using multi-layer feed forward neural network as the plantpsilas nonlinear model is presented. Two algorithm i.e. Newton Raphson and Levenberg Marquardt algorithm are implemented and their results are compared. The details about this implementation are given. The utility of each algorithm is outlined in the conclusion. In using Levenberg Marquardt algorithm, the number of iteration needed for convergence is significantly reduced from other techniques. This paper presents a detail derivation of the neural generalized predictive control algorithm with Newton Raphson and Levenberg Marquardt as the minimization algorithm. A simulation result of Newton Raphson and Levenberg Marquardt algorithm are compared. Levenberg Marquardt algorithm shows a convergence of a good solution. The performance comparison of these two algorithms also given in terms of ISE and IAE.