Shaojian Song

An improved method of DHP for optimal control in the clarifying process of sugar cane juice

Clarifying process of sugar cane juice is a dynamic nonlinear system which has the characteristics of strong non-linearity, multi-constraint, large time-delay, multi-input and other characteristics of complex nonlinear systems. In this paper, Elman neural network is applied to the model of the clarifying process of sugar cane juice. An improved method of dual heuristic programming (DHP) affiliated to the approximate dynamic programming (ADP) family is employed to the optional control of neutralized pH value and purified juice pH value in clarifying process of sugar cane juice. The main advantage of this method is to add an “approximate” neural network, which takes use of the states and actions during delay-time of the system to calculate partial derivative of state with respect to action, consequently it overcomes the difficulty of using the system model to calculate the partial derivative of state with respect to action because of the large lagging time. As a result the DHP algorithm is more suitable for real-time process control and on-line application. Finally, the results of the simulation indicate that the improved DHP method has good control effect.

Study on GA-based Training Algorithm for Extreme Learning Machine

In view of the prediction accuracy of Extreme Learning Machines (ELM) is affected by its input weights and hidden layer neurons thresholds, an improved training method for ELM with Genetic Algorithms (GA-ELM) is proposed in this paper. In GA-ELM, after selection, crossover and mutation of Genetic Algorithm (GA), we will get the optimal weights and thresholds, in initial which are randomly obtained by ELM, then to enhance the generalization performance of ELM. The simulation results show that, compared with other algorithms, the GA-ELM has better prediction accuracy.

Predictive model of production index for sugar clarification process by GDFNN

Clarification process is significant to the cane sugar product, because its production index have direct effect on the output and quality of refined sugar. To maintain the index always in the range of expected value through adjusting operation parameters, an index predictive model is need. In this paper, the principle component analysis(PCA) and other statistical method were employed to deal with massive field data first, then built the generalized dynamic fuzzy neural network(GDFNN) predictive model, and finally the new model was compared with the back propagation(BP) network one on various performances.

Research on water level optimal control of boiler drum based on dual heuristic dynamic programming

Boiler drum system is an important component of a thermal power plant or industrial production, and the water level is a critical parameter of boiler drum control system. Because of non-linear, strong coupling and large disturbance, it is difficult to reach a suitable working state of drum system by using traditional control methods. It is necessary to explore new methods to realize optimal control of drum water level. The back propagation (BP) neural network model of boiler drum system is built in this paper firstly, then the optimal control of the drum system by the dual heuristic dynamic programming (DHP) algorithm is realized, and compared with the heuristic dynamic programming (HDP) algorithm at last. The result shows that the DHP optimization algorithm has good performance in control precision and rejecting process disturbances.

Neuro-controller of cement rotary kiln temperature with adaptive critic designs

The production process of the cement rotary kiln is a typical engineering thermodynamics with large inertia, lagging and nonlinearity. So it is very difficult to control this process accurately using traditional control theory. In order to guarantee the process to be stable, and to produce the high-grade cement clinker, it is important to make the temperature of the sintering zone stable. Artificial neural networks offer a solution to this problem due to their advantages, such as self-organization, self-adaptivity and fault tolerance. This paper introduces a novel nonlinear optimal neuro-controller which is based on adaptive critic design and uses the structure of action-dependant heuristic dynamic programming (ADHDP). The principle of ADHDP is presented. An action network and a critic network are set up in such a way that they basically learn from interactions based on local measurement to optimize the neuro-controller. The ADHDP neuro-controller has a simple frame-work and is independent from the system model. A simulation of the cement rotary kiln is carried out using Matlab/Simulink. The simulation results show that using the ADHDP neuro-controller it is possible to keep the temperature of sintering zone stable in a certain range, and the temperature can meet the requirements of cement clinker production. Simulation results also are presented to show that the neuro-controller with the ACD has the potential to control the cement rotary kiln.

Optimal control for boiler combustion system based on iterative heuristic dynamic programming

Boiler combustion system is a complex nonlinear system which has characteristic of strong coupling and strong-disturbance. It is hard to build accurate mathematical model and achieve optimal control for it. In this paper, radial basis function (RBF) neural network model for boiler combustion system is built based on data driven method firstly, then performing the optimal control of the boiler combustion system via the iterative heuristic dynamic programming (HDP) algorithm, and improving the initial weights of neural network and the utility function. Finally compared with the traditional HDP algorithm in Matlab. The result shows that the optimization algorithm of the iteration HDP based on the RBF neural network gets better in overshoot, convergence speed, steady state error, adaptability and robustness.

Optimization control of rectifier in HVDC system with ADHDP

A novel nonlinear optimal controller for a rectifier in HVDC transmission system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming(ADHDP), a member of the adaptive critic designs family is used for the design of the rectifier neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate dynamic programming(ADP). A series of simulations for a rectifier in dulble-ended unipolar HVDC system model with proposed neurocontroller and conventional PI controller were carried out in MATLAB/ Simulink environment. Simulation results are provided to show that the proposed controller performs better than the conventional PI controller. the current of DC line in HVDC system with the proposed controller can quickly track with the changing of the reference current and prevent the occurrence of the current of DC line collapse when the large disturbances occur.

Project-Based Artificial Neural Networks Development Software and Applications

We have designed a kind of practical artificial neural network development software for ordinary engineering technicians. This software, with graphic interface, not only supports multiple types and algorithms of artificial neural networks, but also supports the IEC 61131-3 International Standard. This article, through three application examples of artificial neural networks, shows the feasibility and the easy implementation of this development software, as well as the realization of artificial neural networks in IEC 61131-3 Standard-based software. It also shows the application value of artificial neural networks development tool and the realistic significance of applying artificial neural networks control in the projects.

An Embedded SoftLogic Control System Based on S3C44BOX and IEC 61131-3 Standard

This paper introduces the theory of embedded Softlogic control system based on S3C44B0X controller and IEC 61131-3 standard. The implementation methods of Softlogic control system based on uClinux and IEC 61131-3 standard is provided, and propose an I/O mapping mechanism to solve the problem of relevancy between common I/O variables defined by programming system and the specific physical I/O interfaces. An embedded single neuron adaptive PSD(proportion, sum and differentiation) control system example is given at last.

Real-time Advanced Intelligent Control Software Package

A real-time advanced intelligent control software package is introduced in detail in this paper. It can be easily used to develop expert control system, fuzzy control system and neural network control system, meanwhile, it can work together with various industrial configuration software and equipments. It will promote the application of intelligent control technology