Since it is a more compact and computationally efficient representation than a Mamdani system, the Sugeno system lends itself to the use of adaptive techniques for constructing fuzzy models. In fuzzy logic there are two well-known algorithms: logarithm Mamdani and Takagi Sugeno these are useful in applications such as microelectronics and power electronics. In the control field, specifically in the DC motor, control can be applied by intelligent control techniques such as fuzzy logic, which is further applied in parallel with conventional control techniques.

One of these methods is fuzzy logic, which is necessary to realize improvements over classical logic and is basically a statement that can be understood as a logical value of 1 or 0. To carry out these development methods, a large database and understanding of control methods are necessary to describe the analysis system to facilitate decision-making. Simulation and experimental results are compared with each other and showed the effectiveness of the proposed control algorithm in terms of fast and smooth dynamic response for the speed control of the real DC motor.In the last decade, the use of various control techniques such as hybrid or artificial intelligence has grown exponentially in the fields of power electronics, control systems, and positioning systems. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. As a simple and fast tuning algorithm, two optimization techniques are used trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. Nonlinear PID Controller, DC Motor, Particle Swarm Optimization, Neural Networks, MATLAB, LabVIEW Abstract Department of Control and Systems Engineering / University of Technology

PID Control with labview 2017