Comparisons of Fuzzy MRAS and PID Controllers for EMS Maglev Train

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnetbased totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller.


Introduction
Magnetic levitation is the process of levitating an item via exploiting magnetic fields. If the magnetic force of enchantment is used, it is recognized as magnetic suspension. If magnetic repulsion is used, its miles referred to as magnetic levitation.
Magnetically Levitated (Maglev) trains fluctuate from traditional trains in that they are levitated, guided and propelled alongside a guide manner by means of a converting magnetic field as opposed to through steam, diesel or electric powered engine.
The magnetic levitation machine is a difficult nonlinear mechatronic machine in which an electromagnetic pressure is needed to suspend an item in the air and it calls for an excessive-overall performance controller to control the modern via the superconducting magnets.
This research is aimed at developing methods of improving efficiency in transportation. Additional applied technologies that may have uses in other applications, from inter-satellite communications, to magnetic field probes. The two main types of maglev Technology are: • Electromagnetic suspension (EMS): Makes use of attractive pressure machine to levitate. Which is a German generation. • Electrodynamic suspension (EDS): uses repulsive force device to levitate. Which is a Japan generation.

Mathematical Models 2.1 Maglev train system mathematical model
The electromagnetic pressure f (i, z), acts on the train, which can be expressed as the subsequent dynamic system in upward course consistent with Newton's law: Where m is the mass of the automobile and g is the gravitational steady. The electromagnetic force The voltage-current relationship for the coil is given by The displacement of the train is measured by using the sensor image-detector that is the output and can be formulated as: Where β is the sensor gain The basic transfer function among the coil input voltage V(s) and the sensor output voltage Vz(s) is given as 35 (()) = " ()) "()) = − * + ' (# + )% , )( ) − * )

The Proposed Controller Design
There are two approaches of control system design.

Outward approach:
Is a manipulate design approach that begins from interior to outward i.e. First the open loop transfer function is shaped by controlling it poles and zeros, adding right control design to the system, so that stable normal transfer function might be achieved.

Inward approach:
Is the reverse of the outward technique i.e. First a preferred closed loop transfer function is designed, and then remedy for required controller.

Stability of maglev train system
The maglev train system model has been represented by a transfer function G(s).
The system has zeros at s = -29 and have poles at s = −56, and s = 56. From this, the system has a pole on the right hand side of the s-plane and this is not stable.

Fuzzy Controller
The fuzzy logic control block diagram is shown in Figure 3 below.

.1 PID Tuning
The ZNFD approach may be tough to perform because it is intricate to modify the advantage till the close-loop system oscillates. A little beyond that outcomes causes instability.
The reaction of automatic tuning is exceptionally exact whilst in comparison to the reaction of Ziegler Nichols. So, automatic tuning is used in matlab is used to stabilize the system. Based at the parameters discovered from automobile tuning, attempt to error method is used until higher result is achieved.

.1 Magnetic force versus current graph
The magnetic force versus current graph of the Maglev train system is shown in Figure 10 below.

Maglev train system simulation response
The simulation output for Maglev train system without controller and Step Response of PID Auto-tuning for Maglev System is shown in Figure 11 and Figure 12

Comparison of the Proposed Controllers
The output response of PID, FUZZY and MRAS Controllers for a step input is shown in Figure 14 below.

Numerical values of the Performance of PID, MRAS and Fuzzy Controllers
The numerical values of the proposed controllers is shown in Table 2 below.

Conclusion
Magnetic levitation system is inherently unstable system, because of the device nonlinearity. The output of the magnetic levitation device is determined and analyzed.
The simulation result showed that the settling time of MRAS controller is smaller than the settling time of PID and Fuzzy Controller. The rising time of MRAS controller is smaller than the rising time of PID and Fuzzy Controller. But the percentage overshoot of PID controller is very good when compared with Fuzzy controller and MRAS controller. And the controller can track the gap change and it could re-arrange itself with the gap change occur by change of time. Finally the simulation result prove the effectiveness of the MRAS controller.