Input observer convergence and robustness: Application to compression ratio estimation

Stricker, Karla; Kocher, Lyle; Alstine, Dan Van; Shaver, Gregory M.

doi:10.1016/j.conengprac.2012.11.009

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…These factors seen in the form of system complexities (e.g., uncertain systems, noisy systems, and so forth) have been considered in many research, and solutions to specific complexities have been proposed. () Nevertheless, research is yet to address situations when all of these factors are combined, also known as the high‐rate dynamics problem. A brief survey on observers is conducted in the next subsections to develop building blocks for a potential solution.…”

Section: Challenges Associated With State Estimation Of High‐rate Dynmentioning

confidence: 99%

“…() For most cases, the HGO is used as an LO‐type estimator with large gain, and the application is for estimating slowly varying states or inputs. () A disadvantage of the HGO is strong chattering when the gain is very large. () Lastly, the robust state estimator guarantees robustness of the designed estimator if time invariant nominal system matrices, constant filter design parameters, and stationary external inputs conditions are met.…”

Section: Challenges Associated With State Estimation Of High‐rate Dynmentioning

confidence: 99%

“…In related studies, Shahrokhi and Morari [52] attributed this problem to the utilization of single observation errors, whereas Khayati and Zhu [51] explained this slow convergence by the complexity of the adaptive law. AOs can be used to estimate states and parameters using Luenberger observer Very fast convergence rates for linear Not applicable, high-rate dynamic [32] systems with precise system models systems are nonlinear and uncertain Sliding mode observer High robustness and improved Not suitable for high-rate [36][37][38][39][40][41] results for inaccurate models but sensitive to dynamic systems choice of gain limiting the convergence rate Extended Kalman filter High accuracy for nonlinear systems Poses challenges for real-time [25,34] with added noise but complex implementation and high-rate applications leading to poor convergence rates High-gain observer Accurate and fast convergence rates Not suitable for high-rate systems [27,45] for estimating slowly varying states or inputs Robust state estimator Guarantees robustness for time Only applicable to stationary [47] invariant systems, constant filter design high-rate dynamic systems parameters, and stationary external inputs input-output measurements, ideal for handling uncertainty in state estimation. [53] Methods include fuzzy logic [54] and neural network estimators.…”

Section: A Path To State Estimation Of High-rate Dynamicsmentioning

confidence: 99%

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Study of input space for state estimation of high‐rate dynamics

Hong

Laflamme

Dodson

2018

Struct Control Health Monit

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Summary 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.

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Section: Challenges Associated With State Estimation Of High‐rate Dynmentioning

confidence: 99%

Section: Challenges Associated With State Estimation Of High‐rate Dynmentioning

confidence: 99%

Section: A Path To State Estimation Of High-rate Dynamicsmentioning

confidence: 99%

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Study of input space for state estimation of high‐rate dynamics

Hong

Laflamme

Dodson

2018

Struct Control Health Monit

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“…In Refs. [9][10][11][12], a simple low-order input observer has been exploited and experimentally validated for several applications in the automotive engine domain. The main contribution of this paper is showing that this IDO can be integrated into practical control schemes and demonstrates the benefits of IDO-based control through two case studies.…”

Section: Introductionmentioning

confidence: 99%

Simple Input Disturbance Observer-Based Control: Case Studies

Polóni

Kolmanovsky

Rohal’-Ilkiv

2017

Journal of Dynamic Systems, Measurement, and Control

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In this paper, the application of an input disturbance observer (IDO)-based control, based on a simple input observer previously proposed and used for engine control, is demonstrated in two case studies. The first case study is longitudinal aircraft control with unmodeled aerodynamic nonlinearities satisfying matching conditions. The second case study is the control of an inverted pendulum on a cart which corroborates the ease of integration of IDO-based control into more complex controllers in situations when the matching condition is not satisfied. Improved robustness is demonstrated on an experimental system including changing the pendulum weight which is shown to have no effect on the overall control performance. In both case studies, if the IDO is not applied, the control performance is poor and leads to unstable operation.

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“…High-rate dynamic systems include added complexities such as large uncertainties in the external loads, high levels of nonstationarities and disturbances, and generation of unmodeled dynamics from rapid changes in mechanical configurations. These complexities have been considered individually in Ref., [9][10][11][12][13][14][15] however, they have yet to be considered altogether.…”

Section: Introductionmentioning

confidence: 99%

Variable input observer for state estimation of high-rate dynamics

et al. 2017

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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.

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Input observer convergence and robustness: Application to compression ratio estimation

Cited by 10 publications

References 29 publications

Study of input space for state estimation of high‐rate dynamics

Study of input space for state estimation of high‐rate dynamics

Simple Input Disturbance Observer-Based Control: Case Studies

Variable input observer for state estimation of high-rate dynamics

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