Research Of Multivariable Fuzzy Neural Network Controller Based On FPGA

Fuzzy control and neural network control, which are the control method of advanced control technology, access to a wide application in the industrial process control. The actual industrial process often has many characteristics involving nonlinear, uncertain and difficult to establish accurate mathematical model, which can not achieve desired control effect by using a single control method. If we can combine the two control methods with each other in their respective merits. It is designed the general control chip of fuzzy neural network and carried out the effective and reasonable control in the process of industrial production.The control method can cut down the developing period of system and reduce the developing cost of system.The research of fuzzy neural network mainly includes the theoretical study of fuzzy neural network, the application research of fuzzy neural network and the achievement technology research of fuzzy neural network. It is emphasized on the implementation technical research of fuzzy neural network, innovatively used of the application and development technology of FPGA-based embedded system to accomplish the research and design of fuzzy neural network controller, and packaged the controller into a dedicated IP core for the other control system.First and foremost, the paper introduced the prorouthly research and analysis of control theory and design algorithms of fuzzy neural network. In addition, it is designed the multivariable fuzzy neural network controller in MATLAB, whose simulation experiment of the particular charged object is carried out, and it is obtained to the ideal control effect. Last but not least, it is investigated the implementation of the algorithm of multivariable fuzzy neural network control based on FPGA, and it is carried out the hierarchal design of the controller, then it is carried on the simulation experiment with the software of Altera's QuartusII 8.1. The results of the two simulation experiments show that the performance of the FPGA-based fuzzy neural network controller is more excellent than that of the fuzzy neural network controller designed in MATLAB. The design module of the system includes fuzzification module, control rule module, defuzzification module, coupling treatment module and weight parameter module.It is followed with the principle of top-down design in the design process, and has been optimized the each module design, then it is carried out the whole system design and synthesis. The design can not only raise the speed of the controller, under the premise of the use of fewer hardware resources, but also improve the control performance of the traditional controller.