Faissal ABDEL-HADY, Sherif ABUELENIN
Bemidji State University,
Bemidji, MN 56601 USA
E-mail: fabdelhady@bemidjistate.edu
Sinai University, Arish, Egypt
(Corresponding author) E-mail: sherif217@ieee.org

Abstract: In this paper, we present the design and simulation of a new fuzzy logic supervisory control approach that is designed to improve the performance of a PID controlled magnetic levitation system, such systems are inherently unstable and require means of control to stabilize their operation; the fuzzy logic controller continuously monitors system variables (error signal, and its derivative) and modifies the parameters of the PID controller to better introduce better system response. Using magnetic levitation eliminates metal friction, and the problems associated with heat dissipation and enables higher speeds, which, in industrial systems, can increase the production rate. The controller is kept as simple as possible so that it can be easily implemented on a low-cost microcontroller chip in the future. A Simulink® model of the magnetic levitation system with the controller is used to simulate and examine the system performance. A noticeable improvement in the performance has been recorded with the integrated controller over the PID alone.

Keywords: Fuzzy supervisory control, Magnetic Levitation, Fuzzy Control, PID Control, Fuzzy Tuning.

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CITE THIS PAPER AS:
Faissal ABDEL-HADY, Sherif ABUELENIN, Design and Simulation of a Fuzzy-Supervised PID Controller for a Magnetic Levitation System, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (3), pp. 315-328, 2008.

1. Introduction

Magnetic levitation systems are systems in which a rotor or a stationary object is suspended in magnetic field. Magnetic levitation of a rotating disk typically incorporates four or more electromagnets to levitate a ferromagnetic disk without contact with the surroundings, where levitation is accomplished through automatic control of the electromagnet coils currents. Position sensors are required to sense the position of the disk, and a controller uses position sensor outputs to apply stiffness and damping forces to the rotor to achieve a desired dynamic response.

Active magnetic levitation systems are being increasingly used in industrial applications where minimum friction is desired or in harsh environments where traditional bearings and their associated lubrication systems are considered unacceptable, as discussed in [6, 10]. Such systems are inherently open-loop unstable, and require means of control to stabilize their operation; this is generally done by creating a closed loop system through using feedback control. The requirement of controllers brings flexibility into the dynamic response of the systems, which can also be designed to compensate for noises and vibrations that would affect the operation. Also, these systems are highly nonlinear, and in order to obtain a transfer function to describe them, number of approximations has to be made; hence, the design of linear controllers can produce the desired dynamic response only for the region in which the linear model was created. Many non-linear control algorithms were introduced in earlier research [7, 8, 9, 10, 11], and a comparison between using linear and non-linear methods of controlling magnetic levitation systems was discussed in [4].

In this paper we introduce the design and simulation of a new supervisory control strategy for magnetic levitation systems that incorporates a fuzzy controller to tune the gains of a discrete PID controller.

Supervisory control is a type of adaptive control since it seeks to observe the current behavior of the control system and modify the controller to improve the performance. It is a multilayer (hierarchical) controller with the supervisory at the highest level; the supervisor controller can use any available data from the control system to characterize the system’s current behavior and generate outputs that are not direct command inputs to the plant. Rather, they dictate changes to another controller that generates these command inputs [17]. Over 90% of the controllers in operation today are PID controllers. This is because PID controllers are easy to understand, easy to explain to others, and easy to implement. Because PID controllers are often not properly tuned (e.g., due to plant parameter variations or operating condition changes), there is a significant need to develop methods for the automatic tuning of PID controllers. The supervisor is trying to recognize when the controller is not properly tuned and then seeks to adjust the PID gains to obtain improved performance. When there is heuristic knowledge available on how to tune PID controllers while in operation, there is the opportunity to utilize fuzzy control methods as the supervisor that tunes or coordinates the application of conventional controllers, this approach shouldn’t be confused with Fuzzy-PID controllers, which are PID controllers realized by fuzzy control methods [16].

Overall, fuzzy PID auto-tuners tend to be very application dependent and it is difficult to present a general approach to on-line fuzzy PID auto-tuning that will work for a wide variety of applications [17].