Zhongda Tian

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.

A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm

The telecommunication and Ethernet traffic prediction problem is studied. Network traffic prediction is an important problem of telecommunication and Ethernet congestion control and network management. In order to improve network traffic prediction accuracy, a network traffic hybrid prediction model was proposed by using the advantages of grey model and Elman neural network, grey model and Elman neural network predictive values were independently obtained, the different weight coefficients of two prediction models were given. In terms of weight coefficients optimization, an improved harmony search algorithm with better convergence speed and accuracy was proposed, the optimal weight coefficients of network traffic hybrid prediction model were determined through this algorithm, two prediction models results were multiplied by the weight coefficients to obtain the final prediction value. The network traffic sample data from an actual telecommunication network was collected as simulation object. The simulation results verified that the proposed network traffic hybrid prediction model based on improved harmony search algorithm has higher prediction accuracy.

Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM

ABSTRACT Short-term wind speed prediction is of importance for power grids. It can mitigate the disadvantageous impacts of wind farms on power systems and enhance the competitiveness of wind power in electricity markets. A short-term wind speed prediction model is proposed. Many useless neurons of incremental extreme learning machine have little influences on the final output, at the same time, reduce the efficiency of the algorithm. In order to solve this problem, based on error minimized extreme learning machine, an improved particle swarm optimization algorithm is proposed to decrease the number of useless neurons, achieve the goal of reducing the network complexity and improving the efficiency of the algorithm. The stability and convergence of the algorithm are proved. The actual short-term wind speed time series is used as the research object. Multistep prediction simulation of short-term wind speed is performed out. Compared with the other prediction models, the simulation results show that the prediction model proposed in this paper reduces the training time of the model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability, meanwhile improve the prediction performance indicators.

LSSVM Predictive Control for Calcination Zone Temperature in Rotary Kiln with IHS Algorithm

The calcination zone temperature control is an important problem in rotary kiln production process. In order to solve this problem, a predictive control method based on improved harmony search algorithm (IHS) and least square support vector machine (LSSVM) is proposed. LSSVM is utilized to bulid the nonlinear predictive model of calcination zone temperature in rotary kiln. The calcination zone temperature can be predicted through input control variable, the error and error correction of output feedback. The performance index function is established by deviation and control variable. An IHS algorithm with better fitness and faster convergence speed is proposed. The optimal control variable can be obtained by rolling optimization through this IHS algorithm. The stability of this predictive control method is proved to be feasible. The simulation and actual experiment results show that the proposed predictive control method has good control performance.