Vibration Control

by Gordon Kraft

Nearly everyone has experienced the annoyance of a long drive with an unbalanced tire, or the whir of a noisy hard drive, or seen the blur in a picture taken from a camera that moved as the shutter closed. If you saw the movie "Hunt for Red October", you know how important submarine underwater vibrations are to the Navy. The Hubbell telescope cannot function if the supporting platform in space is moving. Factory workers are less efficient if they feel machinery vibrations for long periods of time. All of these are examples of unwanted vibrations. Control of these unwanted vibrations is a very important problem.

Most vibration control systems are passive. The rubber in your car engine mounts or in air conditioning ducts are examples. These are called vibration absorbers and by far most vibration reduction systems use these passive elements. In some systems it's important to reduce the vibrations beyond the capability of the passive systems. In these cases, an active feedback control loop is required. Simply stated, the vibration measurements taken from various types of acceleration sensors are processed and then applied to dynamic actuators to apply forces that oppose the vibrations. These systems are usually subject to "ad hoc" adjustments to tune the feedback controller for the particular application. Once the system is tuned the performance of the system remains fixed. That is, it never gets any better as time goes on.

In 1997 we received an NSF grant for $365,000.00 to apply a neural network called CMAC to the area of vibration control. The NSF grant is from the Knowledge Modeling and Computational Intelligence Division of NSF (director is Dr. Paul Werbos). The UNH Robotics Lab version of CMAC has been very successful at other types of control systems such as robotics and signal processing applications. It has advantages over other neural networks such as reduced memory requirements, faster training times and faster real-time control cycles. The key point is that, with CMAC in the control loop, the control system performance will continue to improve with time. As the network accumulates more experience about the system, it is able to continuously improve the control signal to reduce the vibrations more effectively. The network is capable of working with linear or non-linear systems and adjusts itself to changing parameters in the system.

Some of you out there may remember Jim Glynn (UNH MSEE) who now works for Material Systems, Inc. (MSI) in Littleton, MA. His company has formed a relationship with the ECE department to help work on these vibration control projects. MSI will supply funding to support a graduate student and also will provide some of the piezo-ceramic actuators and sensors for the project. His experience with underwater acoustics and real world vibration projects has added a lot to the success of the project.

Currently there are four students working on the project: Paul Tower, a mechanical engineering student; Paul Wheeler and Jeremy Pallotta, electrical engineering graduate students; and Assane Faye, an electrical engineering undergraduate student.