1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

This work presents the analytic and experimental development of piezoelectric actuators as elements of intelligent structures, i.e., structures with highly distributed actuators, sensors, and processing networks. Static and dynamic analytic models are derived for segmented piezoelectric actuators that are either bonded to an elastic substructure or embedded in a laminated composite. These models lead to the ability to predict, a priori, the response of the structural member to a command voltage applied to the piezoelectric and give guidance as to the optimal location for actuator placement. A scaling analysis is performed to demonstrate that the effectiveness of piezoelectric actuators is independent of the size of the structure and to evaluate various piezoelectric materials based on their effectiveness in transmitting strain to the substructure. Three test specimens of cantilevered beams were constructed: an aluminum beam with surface-bonded actuators, a glass/epoxy beam with embedded actuators, and a graphite/epoxy beam with embedded actuators. The actuators were used to excite steady-state resonant vibrations in the cantilevered beams. The response of the specimens compared well with those predicted by the analytic models. Static tensile tests performed on glass/epoxy laminates indicated that the embedded actuator reduced the ultimate strength of the laminate by 20%, while not significantly affecting the global elastic modulus of the specimen.

Use of piezoelectric actuators as elements of intelligent structures

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Damping of structural vibrations with piezoelectric materials and passive electrical networks

A technique has been developed which allows a single piece of piezoelec tric material to concurrently sense and actuate in a closed loop system. The motivation behind the technique is that such a s...

A Self-Sensing Piezoelectric Actuator for Collocated Control:

In Chapter 2 it was determined that a nonlinear multibody model was needed to describe the flight dynamics of the ornithopter. In Chapter 3 the rigid body equations of motion, as well as the gravitational effects, were derived from first principles. The aerodynamics for an arbitrary flapping wing vehicle are difficult to model analytically, and are determined in this work using system identification methods with experimental data. This chapter begins with a presentation of the system identification methods employed herein. Afterwards, tail aerodynamic models are identified from wind tunnel data, and wing aerodynamic models are identified from flight test data. This chapter concludes the nonlinear multibody model of the ornithopter flight dynamics.

James E. Hubbard

Papers

System identification of aerodynamic models