Neural Network Internal Model Control Research Articles

Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

Modelling and control of fouling in submerged membrane bioreactor using neural network internal model control

Membrane bioreactor (MBR) is one of the best solutions for water and wastewater treatment systems in producing high quality effluent that meets its standard regulations. However, fouling is one of the main issues in membrane filtration for membrane bioreactor system. The prediction of fouling is crucial in the membrane bioreactor control system design. This paper presents an intelligence modeling system so called artificial neural network (ANN). The feedforward neural network (FFNN), radial basis function neural network (RBFNN) and nonlinear autoregressive exogenous neural network (NARXNN) are applied to model the submerged MBR filtration system. The simulation results show good predictions for all methods which the highest performance of the model given by RBFNN. Based on the developed models, the neural network internal model control (NNIMC) is implemented to control fouling development in membrane filtration process. Three different control structures of the NNIMC are proposed. The FFNN IMC, RBFNN IMC and NARXNN IMC controllers are compared to the conventional IMC. The RBFNN IMC has a superior performance both in tracking and disturbance rejections.

The Linkage Mechanism with Clearance Modeling Based on Neural Network and its Intelligent Control

Using the traditional control method is difficult to obtain the ideal control result, because the linkage mechanism with clearance is a strongly nonlinear system, and it is difficult to establish the accurate mathematical model. In response to these problems, take the linkage mechanism with clearance for example and establish BP neural network offline modeling, on the basis of the experimental sample data. We separately applied the neural network internal model control and the parameters self-adjusting fuzzy control to reduce the nonlinear error caused by the clearance. The experimental results show that using intelligent control technology, the system stability has been significantly improved, and the system error has been reduced effectively.

Neural Internal Model Control for tracking Unknown Nonaffine Nonlinear Discrete-time Systems under External Disturbances

of nonlinear dynamic plants under external disturbances is presently an active area of research. The main difficulty felt in the tracking of nonlinear dynamic plants under external disturbances is the computational complexity in control design. This paper presents a simple neural network internal model control (IMC) approach for off-line tracking of unknown nonaffine nonlinear discrete time systems subject to external disturbances. The proposed control scheme is based on the neural network plant model and the inverse model. These models are determined using input output data. The final neural network IMC approach, including network plant model and the inverse model, is work off-line for tracking nonaffine nonlinear discrete time systems subject to external disturbances. Simulation results have been presented toward the end of the paper to illustrate the effectiveness of the proposed control strategy for tracking unknown nonaffine nonlinear discrete-time systems with and without external disturbances.

Artificial neural network based modeling and controlling of distillation column system

A Neural Network Internal Model Control (NN- IMC) strategy is investigated, by establishing inverse and forward model based neural network (NN). Further for developing the model has been selected suitable adaptive filter. Two types of NN-based inverse model (i.e. with and without disturbance input) were accurately simulated. The results indicated that the neural networks are capable to establish forward and inverse model rapidly from the couple of input-output open loop data of single distillation column binary system with a good root mean square error (RMSE). The simulation results revealed that NN-IMC with appropriate learning rate - momentum is capable to pursue the set-point changes and to reject the disturbance changes without steady state error or oscillations. NN-IMC with inverse model which contains disturbance input (modified NN-IMC) offer better performance than without it (conventional NN-IMC). International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 177-188

FUZZY LOGIC AND GENETIC ALGORITHMS SUPERVISORS FOR INTERNAL MODEL CONTROL STRATEGY

This paper presents two methods allowing the online adjustment of the filter gain in the internal model control (IMC) strategy. These methods are based on fuzzy logic and genetic algorithms. The IMC strategy needs the direct model and the inverse model of the process. These models can be estimated offline from input-output data. In this work, we have used feed forward Artificial Neural Networks to determine these models. The back propagation algorithm is used to train the neural networks. The neural network internal model control with the proposed supervisors is applied to numerical examples. The performances of the proposed controller are compared to a standard PI controller and to a PI controller with an anticipation action given by the inverse model of the process.