A Review On Implementation of Neuro-fuzzy PID Controller Using FPGA

Neuro Fuzzy (NF) computing is a popular framework for solving complex problems. The combination of neural networks with fuzzy knowledge base helps to reduce the searching space and time for achieving optimal solution. While Proportional-IntegralDerivative controllers are universal control structure and have widely used in Automation systems, Conventional PID controllers are not well for time-delayed linear and nonlinear systems. Thus Neuro-Fuzzy PID Controller is designed to enhance its performance evaluation parameter. This paper describes the design of Neuro-Fuzzy PID Controller using Field Programmable Gate Array. The implementation of Neuro-Fuzzy PID controller using FPGA will help for compactness to improve its speed and accuracy. KeywordsNeuro-Fuzzy System, PID controller, FPGA, ANN, Fuzzy system, ANFIS, VHDL. INTRODUCTION The Fuzzy Logic is closer as to human thinking and in language than conventional logical systems [1]. Fuzzy hardware systems have been developed, including fuzzy rules bonds, fuzzy interface devices, and optical fuzzy inference devices [1]. Fuzzy logic is one of the successful applications in the control engineering field which can be used to control various parameters of the real time systems. The main problem to overcome in applications is the difficulty or uncertainty in fuzzy modeling of the linguistic structure for process [1]. While Artificial Neural Networks are mathematical models inspired from our understanding of biological nervous system [2]. They are attractive as computation devices that can accept a large number of inputs and learn solely from training samples [2]. As mathematical models for biological nervous system, artificial neural networks are useful in establishing relationships between input and out of any kind of the system [2]. That is neural network is collection of artificial neuron. Neural network can learn from data. However, the knowledge learned by neural networks has been difficult to understand . While; it is easy to understand the fuzzy rule based models because it uses linguistic terms and the structure of IF-THEN rules. However, fuzzy logic by itself cannot learn. Since neural networks can learn, it is natural to hybrid fuzzy system and neural network. Hybrid neuro-fuzzy system (NFS) combines artificial neural network and fuzzy logic in a synergetic way [3]. Fuzzy system provide a framework to represent imprecise information and to reason with this kind of information, while neural networks enhance fuzzy systems[3] with the capability of learning from input-output samples; learning is used to adapt parameters of the fuzzy system as a membership function or rule[3]. Integrating these two methodologies, in control can lead to better technologies that take advantage of the strengths of each methodology and at the same time overcome some limitations of individual techniques [2]. Conventional proportional-integral-derivative (PID) controllers are well known and have been extensively used for industrial automation and process control. PID controllers are simple, easy to understand and implement in hardware and software, and do not require a process model for initialization [5] or operation [5]. Many nonlinear processes can satisfactory controlled using PID controllers provided that controller parameters are tuned well. The implementation of PID controllers using microprocessors and DSP chips is old and well known, whereas very little works can be found in the literature on how to implement PID controllers using FPGAs. The Field Programmable Gate Array (FPGA) is one of the most powerful chips among the programmable electronic devices. The preparation and execution of the code with such elements is unique. In the case of FPGA hardware reconfiguration is realized, and during the implementation of an algorithm, the connection of logic elements is changed in the structure of a chip. Thus programming of the FPGA means hardware configuration of the chip. In result parallel calculations are possible. Low costs and high performance lead to many applications of FPGAs in industrial systems. LITERATURE REVIEW The literature survey carried out related to impact in the study of Fuzzy system, neural network, Neuro-Fuzzy system and PID controller. International Journal of Engineering Research and General Science Volume 3, Issue 1, January-February, 2015 ISSN 2091-273