Building a feedforward neural network optimizer in Model Predictive Control Algorithm

This paper presents the possibility of replacing the mathematical optimizer in the Model Predictive Control Algorithm (MPC) with a Feedforward Neural Network Optimizer (FNNO). The optimizer trained offline to reduce the cost function. This maintain the system model of the system, which is essential in MPC to get accepted accuracy. we solve optimization problem faster than the algorithms of traditional optimization, which we built, based on digital computing.

Adaption Of Control Signal Weighting Factor With Measured Disturbance In GPC Technique

In this paper, an adaption mechanism for control signal weighting factor in Generalized Predictive Control (GPC) Technique has been build. This factor changes according to the amplitude of the measured disturbance affecting the acid influent in pHNeutralization process. The main purpose of this adaption is to reduce rigorousness and severity of the manipulated variable of alkaline flow actuator, which result in protecting the actuator from damage, so lengthen its life and shrinking the maintenance costs. The efficiency of the Adaption was observed by calculating the integral of the absolute value of the error (IAE) and the integral of absolute derivative signal (IADS) from simulation results.