Design and implementation are studied for a neural-network-based predictive controller meant to govern the dynamics of non-linear processes. The advantages of using neural networks for modeling non-linear processes are shown together with the construction of neural predictors. The resulting implementation of the neural predictive controller is able to eliminate the most significant obstacles en...

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller ...

This paper presents an explorative work about a Model Predictive Control (MPC) technique. A nonlinear model predictive controller was designed and applied to a fedbatch bioprocess. First, bioprocess and its model are described. Then, a controller based in a nonlinear MPC scheme (Dual Fuzzy Model Predictive Control DF-MPC) is proposed with the aim to test the control of substrate concentration i...

This work gives a short overview of the role that model predictive control has in the development of the advanced wind turbine control algorithms. The advantages of model predictive control compared to conventional controllers involved in wind turbine control are outlined. Wind turbine model predictive speed controller based on identified piecewise affine discrete-time statespace model is desig...

The Proton Exchange Membrane Fuel Cell (PEMFC) control system has an important effect on operation of cell. Traditional controllers couldn’t lead to acceptable responses because of timechange, longhysteresis, uncertainty, strongcoupling and nonlinear characteristics of PEMFCs, so an intelligent or adaptive controller is needed. In this paper a neural network predictive controller have been desi...

In this work we focus on estimation-based networked predictive control of nonlinear systems. We propose an output feedback controller based on the combination of a high-gain observer with a Lyapunov-based model predictive controller that takes data losses explicitly into account both in the controller design and in the implementation. We provide precise bounds on the data loss sequence such tha...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

The control of car following is essential due to its safety and its operational efficiency. For this purpose, this paper builds a model of car following behavior based on ARMAX structure from a real traffic dataset and design a Model Predictive Control (MPC) system. Based on the relative distance and relative acceleration of each instant, the MPC predicts the future behavior of the leader vehic...

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which ...