Sensor Choice for Minimum Error Variance Estimation

Zhang, Minxin; Morris, Kirsten

doi:10.1109/tac.2017.2714643

Cited by 23 publications

(29 citation statements)

References 27 publications

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“…The  ∞ -optimal combination of the sensor measurements is presented in the following method. 12 , and ∈ ℝ >0 that minimize subject to (13), (17), and (18).…”

Section: Sensor Combination Methodsmentioning

confidence: 99%

“…8,9 Different approaches focus on selecting sensors or actuators to optimize open-loop system properties. For example, maximizing the controllability or observability of the open-loop system, [10][11][12][13][14][15][16][17] minimizing parameter estimation error, 18 minimizing the minimum real part of the open-loop system's transmission zeros, 11 or maximizing the pitch, roll, and yaw moments of an aircraft. 19 An alternative to sensor selection is sensor combination, where multiple sensor measurements are combined together to yield a blended measurement that can then be input into a feedback controller.…”

Section: Introductionmentioning

confidence: 99%

“…Different approaches focus on selecting sensors or actuators to optimize open‐loop system properties. For example, maximizing the controllability or observability of the open‐loop system, minimizing parameter estimation error, minimizing the minimum real part of the open‐loop system's transmission zeros, or maximizing the pitch, roll, and yaw moments of an aircraft …”

Section: Introductionmentioning

confidence: 99%

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Optimal linear combination of sensors and actuators to enforce an interior conic open‐loop system

Caverly

2019

Intl J Robust & Nonlinear

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Summary This paper presents techniques to linearly combine the sensor measurements and/or actuator inputs of a linear time‐invariant system to obtain a new system that is interior conic with prescribed bounds. In the optimal sensor combination problem, a desired system output is defined, and in the optimal actuator combination problem, a desired system input is defined, along with a frequency bandwidth in which the desired system input or output should be matched. The simultaneous optimal sensor and actuator combination problem includes desired system outputs and inputs. In all cases, the weighted scriptH2 or scriptH∞ norm of the difference between the system with linearly combined sensors or actuators and the desired system is minimized while rendering the new system interior conic with prescribed bounds. The weighting transfer matrix used in the scriptH2‐ or scriptH∞‐optimization problem is determined by the frequency bandwidth of interest. The individual sensor and actuator combination methods involve linear matrix inequality constraints and are posed as convex optimization problems, whereas the combined sensor and actuator method is an iterative procedure composed of convex optimization steps. Numerical examples illustrate superior tracking performance with the proposed sensor and actuator combination techniques over comparable techniques in the literature when implemented with a simple feedback controller. Robust asymptotic stability of the closed‐loop system to plant uncertainty is demonstrated in the numerical examples.

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“…The  ∞ -optimal combination of the sensor measurements is presented in the following method. 12 , and ∈ ℝ >0 that minimize subject to (13), (17), and (18).…”

Section: Sensor Combination Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal linear combination of sensors and actuators to enforce an interior conic open‐loop system

Caverly

2019

Intl J Robust & Nonlinear

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“…Such combined actuator location and controller optimization problems have been considered in [9], [10], [11], [12]. The dual problem in which the variance of the estimation error is minimized over sensor locations and observers has also been addressed in [13], [3], [4]. An additional problem is that practical implementation requires that the order of the controller (and/or observer) is sufficiently low.…”

mentioning

confidence: 99%

“…In many of the approaches that address the design of a feedback controller (and/or observer), the optimal sensor and/or actuator locations by searching through a discrete set of potential sensor and/or actuation locations, see e.g. [10], [13], [14]. Such an approach does not seem to use the connection to the underlying PDE fully.…”

mentioning

confidence: 99%

Sensor and Actuator Placement for Proportional Feedback Control in Advection-Diffusion Equations

Veldman

Fey

Zwart

et al. 2020

IEEE Control Syst. Lett.

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Sensor location selection for continuous pulp digesters with delayed measurements

2022

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The state estimation and sensor placement for a continuous pulp digester with delayed measurements are investigated. The underlying model of interest is heat transfer in a pulp digester modeled by two coupled hyperbolic partial differential equations and an ordinary differential equation. Output measurements are considered with delay due to the possible low sampling rate. The Cayley-Tustin transformation is utilized to realize model time discretization in a late lumping manner which does not account for any type of spatial approximation or model reduction. The discrete Kalman filter is applied to estimate the system states using the delayed measurements. The selection of sensor location is addressed along with estimator design accounting for the delayed measurements and investigated by minimizing the variance of estimation error. The performance of the state estimator is evaluated, and the sensor placement is analyzed through simulation studies, which offers a planning view of sensor location in industrial applications.

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Sensor Choice for Minimum Error Variance Estimation

Cited by 23 publications

References 27 publications

Optimal linear combination of sensors and actuators to enforce an interior conic open‐loop system

Optimal linear combination of sensors and actuators to enforce an interior conic open‐loop system

Sensor and Actuator Placement for Proportional Feedback Control in Advection-Diffusion Equations

Sensor location selection for continuous pulp digesters with delayed measurements

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