The Study Of Fuzzy Neural Network Predictive Control Algorithm Based On GA-PSO

Predictive control technology is one of important branches of intelligent control technology, is a computer control algorithm which is built on actual industrial process and develops along with it. It has been always a focus in control field. The linear predictive control has been widely used in the field of practical application and has created welcome economic benefits, which is based on the advantage of predictive control, such as the stronger robustness, lower model requirements and good ability to deal with time delay and time-varying problems. However, the real system is characterized by the strong nonlinear, coupling, time-varying and high control requirements in the actual process of industrial control. The mature linear prediction has been difficult to achieve satisfactory control effect. Thus, the study of non-linear predictive control has becomes a focus topic in control areas.In order to meet the complex requirements of the actual control system, it is the new trend for predictive control to develop towards intelligence, which needs integrate intelligent control technology and predictive control theory into composite intelligent predictive control algorithm, and open up a new way to solve the control problem of complex multivariable system. The approximation capability and self-learning ability of neural network is the theoretical basis of its successful application, and human language is easily used to understand and express in fuzzy logic system. The combination of them can enhance their advantages and avoid disadvantages, and expand the range of processing information of neural network.In addition, it can gradually deal with uncertain information and inaccurate information while can only deal with accurate information in the past. Moreover, the fuzzy rules and membership functions can be automatically obtained and the adaptive capacity of the system is improved. From the aspects of the basic characteristics of predictive control, fuzzy neural predictive control algorithm based on the composite particle swarm optimization algorithm is presented against the advantages and disadvantages of intelligent control technology. In order to make the system theory more perfect and progressive, the main branches of intelligent technology, including predictive control, fuzzy logic, neural networks, and the basic principles of swarm intelligence optimization algorithm, its algorithm flow and application and the comparisons between intelligent technology branches, are introduced in detail.Then, the simulation study for the new algorithm is conducted based on MATLAB. The tracking performance, robustness, anti-disturbance, prediction accuracy and the results of iterative process of optimization are analyzed particularly, and the comparison with the standard generalized predictive control algorithm, neural network predictive control algorithm and fuzzy neural network predictive control algorithm is made respectively. The results indicate that the proposed algorithm in this paper has the characteristics of well control performances, higher prediction accuracy, well convergence and tracking performance, and stronger robustness, which are satisfactory and a certain reference value and guiding significance for the actual industrial production.