The non-linearity and constantly changing cross section of the conical tank are the immense challenge in process control and it cannot be effectively controlled by the linear PID controllers. Hence, an attempt is made here to adapt the PI controller suitable to control the conical tank by two methods namely an adaptive PI controller and neural network based PI controller, which are designed by ...

1PG Scholar,Vimal Jyothi Engineering College,chemberi,Kannur ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Design of multivariable control system is very essential in the process industries. The four tank system is a benchmark system. So disturbances are essential to analyze the performance of...

This chapter describes the design of controllers for the ideal CSTR using soft computing techniques namely, genetic algorithm and fuzzy logic. In this chapter, an attempt has been made to obtain the optimal PI controller parameters for each operating region using GA technique. GA based PI implements the characteristics of global optimization to optimize the PI control parameters Kp and Ki to pr...

This work deals with design, modeling and simulation of the PI & the Fuzzy controlled DAB DC to DC converter systems. Open loop system is simulated with step change in input voltage. The output voltage in closed loop system is regulated using PI and Fuzzy controllers. The response of these two systems are compared. This converter has advantages like low EMI,less switching loss and improved dyna...

This paper proposes the neural network solution to the indirect vector control of three phase induction motor including an adaptive neuro fuzzy controller. The basic equations and elements of the indirect vector control scheme are given. The proposed control scheme is realized by an adaptive neuro-fuzzy controller and two feed forward neural network. The neuro-fuzzy controller incorporates fuzz...

An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI contr...

Original scientific paper This paper presents the use of proportional integral (PI), proportional integral derivative (PID) and fuzzy logic (FL) speed controller techniques in the permanent magnet synchronous motor (PMSM) drive. PI and PID controllers require precise linear mathematical model of the system. On the other hand, FL needs linguistic description of the system. The dynamic response o...

In this paper, a new formula-based fuzzy proportional-integral (FPI) controller is proposed. This controller is based on the conventional proportional-integral (PI) controller. The proposed FPI controller has a linear structure similar to that of the conventional controller, and the operation of the proposed controller is based on analytical formulas. The structure is designed by using three me...

This study proposes a soft computing speed controller where recurrent neural network speed controller is trained by genetic extended kalman filter, based on a predetermined genetic Proportional Integral (PI) controller. A genetic PI gives to this proposed speed controller exact values without mathematical equations, unlike the classical PI who is need for it necessary. With this proposed method...