Programmable Device Circuits and Systems

Podugu Srinivasarao ¹, P. Subbaiah ²

Affiliations
1 ECE Department, Dr. M.G.R. University, Chennai, Tamilnadu, IN
2 E&I Department, Dr. M.G.R. University, Chennai, Tamilnadu, IN

Abstract

In a multivariable process mathematical models of the physical system have gained importance due to the complex interactions in the system. All the parameters used in a model cannot be determined accurately. However, such processes are very often available in power plants, chemical plant and many other fields. The major problem in a multivariable process is that loop interaction can arise and cause difficulties in feedback control design. This problem can be solved using centralized (like LQR, multivariable predictive control, IMC, fuzzy logic controller…) or decentralized controllers. The decentralized control is widely used in industrial plant. One of the decentralized techniques is decoupled method, which used to eliminate the interaction of diagonal loops and can be minimized an MIMO into SISO system. The basic and well known method for MIMO system is a two input two output (TITO) system.
In this paper, a new decoupler method with auto tuning of PID controller for quadruple tank process is proposed. It consist of four inter connected water tanks and two pumps as shown in figure 1. The parameters of PID controller are designed based on stability and speed of response, which are defined in terms of the damping factor and settling times. When damping factor and settling time of the controlled system has been selected the equivalent initial P, I, D values is obtained.

Keywords

PID Controllers, MIMO System, Auto Tuning, Quadruple Tank Process.

Full Text

     

Ramesh Palakeerthi ¹, P. Subbaiah ²

Affiliations
1 Electrical and Electronics Engineering Department, Dr. M.G.R. University, Chennai, Tamilnadu, IN
2 Department of Electronics and Instrumentation, Dr. M.G.R. University, Chennai, Tamilnadu, IN

Abstract

Switched reluctance motor (SRM) drives are used in various applications due to their simplicity, reliability and low cost schemes. It required rotor position feedback to operate and used for high concern in many applications. Thus, a major consideration is the robustness of position estimation schemes when sensorless SRM drive control is employed. The control of SRM using a tuned Fuzzy Logic Controller (FLC) along with neural estimator is done in this paper. The structure hierarchy and computational complexity of the controller is simplified by reducing the number of fuzzy sets in the function without losing the system performance. It uses the FLC for controlling the SRM speed. The FLC performs a PI-like control strategy, giving the current reference variation based on speed error and its change. In this work, we considered the speed variations with FLC and neural estimated rotor angle corresponding to load variables in response to changes in set points.

Keywords

Switched Reluctance Motor (SRM), Fuzzy Logic Controller (FLC), Non Linear Analysis (NIA), and Artificial Neural Network (ANN).

Full Text