System identification,health monitoring,and control design of smart structures and materials

In recent years, smart structures and materials have been adopted from many engineering fields because the performance of structural systems and materials can be improved without either significantly increasing the system mass or requiring high cost of control power.^1,2 To implement such a smart control technology into complex systems, one of the most important but challenging tasks in smart system realization is the development of a mathematical model for system responses that will allow control system design and diagnosis procedures to be carried out in a timely manner. A large number of studies have been performed attempting to estimate behavior of smart system dynamics, using sets of input and output measurement, called system identification (SI).^3,4

SI can be classified as parametric and non-parametric approaches. Parametric methods are used to determine a finite number of parameters such as mass, stiffness, and damping ratio, which are physical quantities of systems. In general, in order to identify an accurate system model using such a parametric approach, a sufficient number of modal parameters must be obtained. However, nonparametric methods determine infinite number of parameters and estimate the model parameters without full understanding of physical systems. The nonparametric method trains measured data to predict the system response even though the identified model does not directly represent the physical quantities. In other words, the dynamic system model can be determined even when little information on the system is provided. Furthermore, the nonparametric SI can be applied to input data or output data or both input and output data sets. In particular, the output data–based SI method has become of great significance in assessing dynamic systems since the input data are not readily available. Using the output SI approach, it is possible to identify the dynamic properties of the system in real operating conditions where the loading conditions are unknown. ⁵

Control systems are classified into three parts: passive, active, and semi-active (or called smart). In particular, the smart control system has been paid a great attention from a variety of engineering field because the smart control system combines the best features of both active and passive control systems. The materials that are usually used to implement the smart structure are piezoelectrics, shape memory alloys, electrostrictive, magnetorheological materials, and polymer gels, among others. ¹ Selection and design of a control algorithm for optimal operation of control devices are very important for improving the performance of the smart structure and material systems.

However, the performance of the smart control systems can degrade in the presence of sensor/actuator faults and/or system damage. To address the aforementioned issues, system health monitoring (SHM) has become increasingly important for complex systems because damage affects the current or future performance of the systems. SHM can provide information when the systems experience any significant change or damage. SHM improves the safety and reliability of critical systems by detecting the damage before they reach a critical state. It also allows rapid damage assessment. In order to practice SHM more efficiently, engineers and researchers have developed various global and local approaches. ⁶

With these in mind, the aim of this Special Collection is to provide an opportunity for engineers to propose their latest theoretical and practical achievements in SI, health monitoring, and control system design of smart materials and structures under a variety of environmental forces.