Experimental Robust Control of Structural Acoustic Radiation

This work addresses the design and application of robust controllers for structural acoustic control. Both simulation and experimental results are presented. H∞ and μ-synthesis design methods were used to design feedback controllers which minimize power radiated from a panel while avoiding instability due to unmodeled dynamics. Specifically, highorder structural modes which couple strongly to the actuator-sensor path were poorly modeled. This model error was analytically bounded with an uncertainty model, which allowed controllers to be designed without artificial limits on control effort. It is found that robust control methods provide the control designer with physically meaningful parameters with which to tune control designs and can be very useful in determining limits of performance. Experimental results also showed, however, poor robustness properties for control designs with ad-hoc uncertainty models. The importance of quantifying and bounding model errors is discussed. Research Engineer, [email protected] Research Engineer Assistant Professor, Member AIAA Assistant Research Professor, Member AIAA Copyright c ©1998 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental Purposes. All other rights are reserved by the copyright owner. Introduction The goal of this work was to apply modern robust control methods to a structural acoustic experiment, and investigate the advantages of robust control in this setting. Acoustic systems are often characterized by high modal density plants which extend well into and even beyond the controller’s bandwidth. Modern control methods, such as LQR/LQG, have been successfully applied to the experimental control of high-order, structural acoustic systems (Vipperman and Clark, 1997). However, when the plant model is uncertain it is difficult to analytically determine the limits of achievable performance. Controllers must be designed with different levels of aggressiveness and experimentally tested to determine which will remain stable. Robust control offers the potential to determine, based on uncertainty models, if a controller is likely to destabilize the plant and to design optimal controllers which obtain performance objectives while retaining stability in the presence of model errors. In modern control methods, such as LQR/LQG, the performance objective and system constraints are bundled into one cost function, for which an optimal system can be determined. These cost functions and the resulting controllers, however, assume accurate knowledge of the plant. In practice it is necessary to bound control effort or to invoke fictitious noise sources in order to obtain an optimal controller which is tolerant of model errors. Guide-