Hybrid MATLAB and LabVIEW with T‐S Cloud Inference Neural Network to Realize a Flatness Intelligent Control System

Abstract

The flatness control system is a multivariate and complex system with uncertainty of parameters. General modeling approaches can not satisfied the high precision demand of rolling process. And it is difficult to establish a precise mathematical model of the rolling mill. In this paper, T-S cloud inference neural network is proposed. It is constructed by cloud model and T-S fuzzy neural network. The uncertainty of cloud model for processing data and the rapidity of fuzzy logic are both taken into account synthetically. And it is applied for 900HC reversible cold rolling mill. A Virtual Flatness Intelligent Control System (VFICS) is successfully developed by integrating hybrid MATLAB and LabVIEW with T-S cloud inference neural network. VFICS is a virtual experiment system, which has realized flatness pattern recognition, flatness prediction and flatness control. Genetic algorithm (GA) and particle swarm optimization (PSO) are used to train the network. Simulation results show that control effect of GA has higher accuracy and faster convergence and it is suitable for controller design. They are provided to confirm the performance and effectiveness of the proposed control method.

Flatness Intelligent Control Based on T-S Cloud Inference Neural Network

“…The future work will concentrate on flatness control system of the visual. 29) The flatness control interface is utilized to simulate the whole rolling process. And the control results will be showed lively and convenient with pressing a button.…”

Mechanical Performance‐Based Optimum Design of High Carbon Pearlitic Steel by Particle Swarm Optimization

“…More recently, there have been a number of researches concentrating extremely on alterations to the optimal chemical composition selection through the artificial intelligence technology. [20][21][22][23][24][25] In particular, many kinds of optimization techniques have been proposed, such as simulated annealing, [26] genetic algorithm, [27,28] particle swarm optimization (PSO), [29][30][31][32] teaching-learning-based optimization algorithm, [33] Jaya algorithm, [34,35] cuckoo search algorithm, [36] and so on. In these algorithms, PSO is the easiest to implement and possesses excellent swarm search ability to obtain the best optimal section of component, which is developed based on the social behavior among members of a specific species to seek the best food sources.…”