Chaotic diagonal recurrent neural network

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.

Nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

Application of Diagonal Recurrent Neural Network toDC Motor Speed Control Systems

A new kind of dynamic neural network--diagonal recurrent neural network (DRNN) and its learning method and architecture are presented. A direct adaptive control scheme is also developed that is applied to a DC (Direct Current) speed control system with the ability to auto-tune PI (Proportion Integral) parameters based on combining DRNN with PI controller. The simulation results of DRNN show better control performances and potential practical use in comparison with PI controller.

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.