Variable Input Space Observer for Structural Health Monitoring of High-Rate Systems

Some engineering systems experiencing high-rate dynamic events, including air bags, debris detection, and active blast protection systems, could benefit from high-rate real-time observers for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper discusses critical challenges in designing highrate observers, and argues that adaptive observers are promising at the task of state estimation, and that they could be applied at high-rates provided the development of strategies to substantially increase their convergence. A novel adaptive observer termed a variable input observer (VIO) is studied for its performance and its application for fault detection. The VIO is designed with an adaptive input space, where the hyperspace and coefficients of the state estimation function can change based on the complexity of the dynamic input. Results show good convergence of the VIO versus fixed input strategies

Introduction to State Estimation of High-Rate System Dynamics

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model

Input space dependent controller for civil structures exposed to multi-hazard excitations

A challenge in the control of civil structures exposed to multiple types of hazards is in the tuning of control parameters to ensure a prescribed level of performance under substantially different excitation dynamics, which could be considered as largely uncertain. A solution is to leverage data driven control algorithms, which, in their adaptive formulation, can self-tune to uncertain environments. The authors have recently proposed a new type of data-driven controller, termed input space dependent controller (ISDC), that has the particularity to adapt its input space in real-time to identify key measurements that represent the essential dynamics of the system. Previous studies have focused on time delay formulations, where the adaptive control rule would use time delayed measurements as inputs. In this configuration, termed variable multi-delay controller (VMDC), the time delay itself was adaptive, which provided the input space dependence capabilities. However, the size, or embedding dimension, of the input space was kept constant. In this paper, the authors formulate and study a strategy to also have the embedding dimension vary, therefore providing full adaptive input space capabilities. This generalization of the ISDC algorithm will allow the controller to adapt to excitations with higher levels of chaos, such as a seismic event. The performance of ISDC under multi-hazard excitations is first investigated on a single-degree-of-freedom system and compared with the previously developed and demonstrated VMDC. Results show that the adaptive embedding dimension provides significantly enhanced mitigation performance. After, the ISDC performance is assessed on two benchmark buildings equipped with a semi-active friction device and subjected to non-simultaneous multi-hazard excitations (wind, blast and earthquake). Results are compared with a sliding mode controller, where the ISDC is shown to provide better mitigation capabilities

Study of Input Space for State Estimation of High-Rate Dynamics

High‐rate dynamic systems are defined as systems being exposed to highly dynamic environments that comprise high‐rate and high‐amplitude events. Examples of such systems include civil structures exposed to blast, space shuttles prone to debris strikes, and aerial vehicles experiencing in‐flight changes. The high‐rate dynamic characteristics of these systems provides several possibilities for state estimators to improve performance, including a high potential to reduce injuries and save lives. In this paper, opportunities and challenges that are specific to state estimation of high‐rate dynamic systems are presented and discussed. It is argued that a possible path to design of state estimators for high‐rate dynamics is the utilization of adaptive data‐based observers but that further research needs to be conducted to increase their convergence rate. An adaptive neuro‐observer is designed to examine the particular challenges in selecting an appropriate input space in high‐rate state estimation. It is found that the choice of inputs has a significant influence on the observer performance for high‐rate dynamics when compared against a low‐rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision‐making process in a closed‐loop system in making bad judgments

Variable input observer for state estimation of high-rate dynamics

High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO’s potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy

Robust Variable Input Observer for Structural Health Monitoring of Systems Experiencing Harsh Extreme Environments

Systems experiencing events in the order of 10μs-10ms timescales, for instance highrate dynamics or harsh extreme environments, may encounter rapid damaging effects. If the structural health of such systems could be accurately estimated in a timely manner, preventative measures could be employed to minimize adverse effects. Previously, a Variable Input Observer (VIO) coupled with a neuro-observer was proposed by the authors as a potential solution in monitoring their structural health. The objective of the VIO is to provide state estimation based on an optimal input space allowed to vary as a function of time. The VIO incorporates the use of mutual information and false nearest neighbors techniques to automatically compute the time delay and embedding dimension at set time intervals. The time delay and embedding dimensions are then used to vary the input space to achieve optimal performance for the estimator based on the observed measurements from sensors. Here, we augment the VIO with a smooth transitioning technique to provide enhanced robustness. The performance of the algorithm is investigated using experimental data obtained from a complex engineering system experiencing a harsh extreme environment. Results show that the enhanced VIO incorporating a smooth transitioning input space outperforms the previous VIO strategies which allowed rapid input space adaptation