Wang Dong-feng

Robust modified projective synchronization of fractional-order chaotic systems with parameters perturbation and external disturbance

Based on fractional-order Lyapunov stability theory, this paper provides a novel method to achieve robust modified projective synchronization of two uncertain fractional-order chaotic systems with external disturbance. Simulation of the fractional-order Lorenz chaotic system and fractional-order Chens chaotic system with both parameters uncertainty and external disturbance show the applicability and the efficiency of the proposed scheme.

Synchronization of uncertain fractional-order chaotic systems with disturbance based on a fractional terminal sliding mode controller

This paper provides a novel method to synchronize uncertain fractional-order chaotic systems with external disturbance via fractional terminal sliding mode control. Based on Lyapunov stability theory, a new fractional-order switching manifold is proposed, and in order to ensure the occurrence of sliding motion in finite time, a corresponding sliding mode control law is designed. The proposed control scheme is applied to synchronize the fractional-order Lorenz chaotic system and fractional-order Chen chaotic system with uncertainty and external disturbance parameters. The simulation results show the applicability and efficiency of the proposed scheme.

Controller design for networked control systems based on neural network prediction

To the networked control systems in which network-induced delay is time-vary and less than one sample time, a neural network predictive control method is presented to compensate the random delays in this paper. Genetic algorithm is used to optimize parameters. Neural network is introduced to do prediction and optimization calculation. And the uncertainty of the delay is transformed to parametrical uncertainties. Simulation result shows that this control method can evidently improve the control quality with increasing time delay, and has stronger adaptability.

Fuzzy identification method in nonlinear system based on G-K clustering algorithm

In accordance with the problems that the algorithm is too complex in the past fuzzy modeling methods, this article propose a new method of fuzzy modeling for nonlinear system. The method is simple and powerful. In this method, the premise configuration and parameter of this fuzzy model is decided by G-K fuzzy clustering algorithm, and succedent parameter of fuzzy model is identified by orthogonal least square. Finally the effectiveness and practicability of this method is demonstrated by the simulation result of the Box-Jenkins gas furnace data.

Adaptive generalized functional synchronization of chaotic systems with unknown parameters

A universal adaptive generalized functional synchronization approach to any two different or identical chaotic systems with unknown parameters is proposed, based on a unified mathematical expression of a large class of chaotic system. Self-adaptive parameter law and control law are given in the form of a theorem. The synchronization between the three-dimensional Rossler chaotic system and the four-dimensional Chens hyper-chaotic system is studied as an example for illustration. The computer simulation results demonstrate the feasibility of the method proposed.

Optimal load economic distribution research and engineering realization

Nowadays, the difference between peak and lowest of load in electricity market is increasing. Central command requires the power plant to complete the load demand quickly. For this situation, we use particle swarm optimization to solve load economic distribution. We change the weight between speed and economy within the plant, taking into account the auxiliary running. Make the power plant keep up with the central command and keep the unit running at lower coal consumption. Tests show that: Our algorithm is stable and reliable. And we have developed thermal power plant load distribution software, which has been put into commercial operation.

PSO and RBF network-based Wiener model and its application to system identification

In this paper, a new kind of Wiener model structure is introduced, which is realized by using the mapping function of neural networks. The model uses the linear dynamic neurons and a RBF network to express one Wiener models dynamic linear part and static nonlinear part respectively. The parameter identification for the new Wiener model adopts the unified identification method. The learning of parameters includes two cycles that the inner-cycle is executed by gradient training methods based on the BP thought and the outer-cycle uses the PSO (Particle Swarm Optimization) algorithm. The training method based on unified identification makes the new Wiener model converge to the steady state along the expected direction with a small error in a short time. The Wiener model is applied to the identification of the famous Box and Jenkins gas CO2 density, and the simulation results show that the method proposed in this paper is effective.

Deviation propagation and reduction analysis for multi-operational machining processes

Product dimensional quality has been one of the most important challenges for machining processes. However, current practice in multi-operational machining is a monitoring or inspection oriented measurement strategy which could not provide solutions for deviation reduction while deviation propagation and compensation are taken into account. While the deviation of some quality characteristic is not satisfactory and could not be controlled in the last operation, a partial least squares regression method is proposed to set up the relations between upstream operations and the last one. The regression result helps engineers to identify key processes with respect to their impacts on the last one, and to figure out possible solutions to reduce the deviation and thus makes the quality characteristic achieve the acceptable level. An industrial case is presented to illustrate our proposed methodology.