Variable input observer for state estimation of high-rate dynamics

Abstract

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 VIObased 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. KeywordsHigh-rate dynamics, input space, adaptive observer, neural network, structural health monitoring ABSTRACT 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 VIObased 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

“…Structural health monitoring of systems experiencing harsh extreme environments is a complex task. Research has shown these systems include multiple system complexities such as highly nonlinear and nonstationary behaviors [2] for which fixed observers are difficult to apply [3] An alternative is to use adaptive observers, but at the cost of slower convergence rates. Literature focuses on accelerating the convergence of adaptive observers, with the vast majority of efforts on the state estimation function.…”

“…[4,5,6] for instance. Instead, the authors have proposed to use an adaptive input space to accelerate convergence, a strategy termed VIO [1,2].…”

“…Prior work from the authors on the VIO demonstrated its potential on a two degrees-of-freedom (2DOF) system, where the VIO was combined with a time delay function for state estimation [1]. The VIO was improved in [2] by using a wavelet neuro-observer instead of a time-delay function and implemented on realistic data, which produced a better set of dynamic functions for the representation. This method was compared with a fixed input strategy.…”