Optimal sensor and actuator locations in distributed parameter systems

Omatu,; Seinfeld,

doi:10.1109/cdc.1987.272706

Cited by 4 publications

(3 citation statements)

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“…(10) where is the location of a state fault, , and represent state, actuator and sensor fault functions respectively. The fault functions are described by , (11) where represents the time when a fault occurs The term represents the time profile of the fault defined by with constant denoting the growth rate of the fault. The following standard assumptions are required in order to proceed.…”

Section: Notation and Linear System Descriptionmentioning

confidence: 99%

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Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

2023

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This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

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Section: Notation and Linear System Descriptionmentioning

confidence: 99%

“…Because of their distributed nature, the ODE representation cannot describe the DPS behavior [10] and they are usually modeled by partial differential equations (PDEs). Fault diagnosis of DPS is more complicated and challenging when compared to LPS since the system parameters are defined over a continuous range of both time and space [11].…”

Section: Introductionmentioning

confidence: 99%

Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

2023

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“…, u km (t)] T and the respective system response as y k (x, t; θ). Finally, ε k ( • , • ) denotes the measurement noise customarily assumed to be zero-mean, spatially uncorrelated and white Gaussian process [16,27].…”

Section: Sensor Selection Problem In Context 21 Model Of System and M...mentioning

confidence: 99%

Optimal sensor selection for prediction-based iterative learning control of distributed parameter systems

Patan

Klimkowicz

Patan

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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The purpose of this study is to develop an effective computational scheme to solve the optimal tracking control problem for repeated trials in distributed-parameter system in the situation where quantity under control cannot be observed directly. In such situations, the reliability of model predictions becomes more important than the accuracy of model parameters, because the ultimate objective in model-based control is the prediction or forecast of the system states. Particularly, given a finite number of possible spatial locations at which sensors may reside, we select gaged sites so as maximize the prediction accuracy. For that purpose, an suitable output criterion is proposed as a measure of the prediction accuracy and the sensor selection problem formulated in terms of optimization task. To solve it, a specialized technique is adopted based on relaxation of the original discrete optimization problem which amounts to operating on the density of sensors in lieu of their individual positions. As a result, a simple and effective exchange algorithm is outlined to select the gaged sites. Then, the measurement schedule providing the most informative system observations is further incorporated into the adaptive control scheme based on iterative learning control technique for effective solution of underlying tracking control problem. The proposed approach is verified by numerical experiments on the model of the friction welding process of two aluminum plates.

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Optimization of Sensor and Actuator Locations and Their Sensitivity Analysis

Omatu

1990

Control of Distributed Parameter Systems 1989

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Optimal sensor and actuator locations in distributed parameter systems

Cited by 4 publications

References 0 publications

Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

Optimal sensor selection for prediction-based iterative learning control of distributed parameter systems

Optimization of Sensor and Actuator Locations and Their Sensitivity Analysis

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