Actuator failure detection in the control of distributed systems

Baruh, H.

doi:10.2514/3.20088

Cited by 27 publications

(21 citation statements)

References 10 publications

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“…To investigate the general behavior of each objective function, we choose four initial actuator location sets as i) [1.66, 3.33. 5.00, 6.67, 8.33], ii) [1.00, 3.00, 5.00, 7.00, 9.00], iii) [3.00, 4.00, 5.00, 6.00, 7.00], and iv) [2.00, 3.00, 4.00, 5.00, 6.00] and carry out the optimization. The corresponding local minima are given in Table 5.…”

Section: Point Force Actuatorsmentioning

confidence: 99%

Actuator placement in structural control

Choe

Baruh

1992

Journal of Guidance, Control, and Dynamics

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The placement of force and torque actuators for structural control problems is considered. Objective functions are defined based on the elements of the actuator influence matrix, and optimization studies are conducted. The performance of the control is compared. The results indicate that a relatively even distribution of the actuators gives satisfactory results, whereas a close spacing of the actuators leads to excessive fuel and energy use. In all cases, several evenly spaced actuator distributions are found to be suitable. In addition, torque actuators are found to be less desirable than force actuators because they excite the higher modes to a greater degree. The efficiency of piecewise-continuous actuators is analyzed. Piecewise-continuous actuators are more realistic models than point actuators and they reduce stress levels. However, if their contact area is too large, they use more fuel.

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Section: Point Force Actuatorsmentioning

confidence: 99%

Actuator placement in structural control

Choe

Baruh

1992

Journal of Guidance, Control, and Dynamics

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“…Examples of earlier works in this direction include the development of fault detection schemes using approximate linear models (e.g., [12], [13]) and the use of hybrid system formulations to develop stability-based and performance-based controller reconfiguration strategies to compensate for faults (e.g., [14]). More recently, we developed in [15], [16] a unified framework for the integration of model-based FDI and control system reconfiguration for distributed processes modeled by nonlinear parabolic PDEs with control constraints and actuator faults.…”

Section: Introductionmentioning

confidence: 99%

Robust diagnosis and fault-tolerant control of uncertain distributed processes with limited measurements

Ghantasala

El‐Farra

2008

2008 American Control Conference

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This work develops a robust fault detection and isolation (FDI) and fault-tolerant control (FTC) structure for distributed processes modeled by nonlinear parabolic PDEs with control constraints, time-varying uncertain variables and a finite number of output measurements with limited accuracy. To facilitate the controller synthesis and fault diagnosis tasks, a finite-dimensional system that approximates the dominant dynamic modes of the PDE is initially derived and transformed to a form where each dominant mode is excited directly by only one actuator. A robustly stabilizing bounded output feedback controller is then designed for each dominant mode. The controller synthesis procedure facilitates the derivation of (1) an explicit characterization of the fault-free behavior of each mode in terms of a time-varying bound on the dissipation rate of the corresponding Lyapunov function which accounts for the uncertainty and measurement errors, and (2) an explicit characterization of the robust stability region where constraint satisfaction and robustness with respect to uncertainty and measurement errors are guaranteed. Using the fault-free Lyapunov dissipation bounds as thresholds for FDI, the detection and isolation of faults in a given actuator is accomplished by monitoring the evolution of the dominant modes within the corresponding stability region and declaring a fault when the threshold is exceeded. Robustness of the FDI scheme to measurement errors is ensured by confining the FDI region to an appropriate subset of the stability region, and enlarging the FDI thresholds appropriately. It is shown that these safeguards can be tuned by appropriate selection of the sensor configuration. Finally, the implementation of the FTC architecture on the infinite-dimensional system is discussed and the proposed methodology is demonstrated using a diffusion-reaction process example.

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“…Compared with the extensive body of literature on fault diagnosis and fault-tolerant control for lumped parameter systems described by ordinary differential equations (e.g., see [1], [2], [3]), results for spatially-distributed systems have been limited (e.g., see [4], [5], [6]). Major bottlenecks in the design of fault-tolerant control systems for distributed parameter systems include the infinite-dimensional nature of these systems, as well as their complex dynamics characterized by nonlinearities and uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Fault-tolerant control of sampled-data nonlinear distributed parameter systems

Ghantasala

El‐Farra

2010

Proceedings of the 2010 American Control Conference

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This work presents an integrated fault detection and fault-tolerant control architecture for spatially-distributed systems described by highly-dissipative systems of nonlinear partial differential equations with actuator faults and sampled measurements. The architecture consists of a family of nonlinear feedback controllers, observer-based fault detection filters that account for the discrete measurement sampling, and a switching law that reconfigures the control actuators following fault detection. An approximate model that captures the dominant dynamics of the infinite-dimensional system is embedded in the control system to provide the controller and fault detection filter with estimates of the measured output between sampling instances. The model state is then updated using the actual measurements whenever they become available from the sensors. A sufficient condition for stability of the sampled-data nonlinear closed-loop system is derived in terms of the sampling rate, the model accuracy, the controller design parameters and the spatial placement of the control actuators. This characterization is used to derive rules for fault detection and actuator reconfiguration. The results are demonstrated through an application to the problem of stabilizing the zero solution of the Kuramoto-Sivashinsky equation.

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Actuator failure detection in the control of distributed systems

Cited by 27 publications

References 10 publications

Actuator placement in structural control

Actuator placement in structural control

Robust diagnosis and fault-tolerant control of uncertain distributed processes with limited measurements

Fault-tolerant control of sampled-data nonlinear distributed parameter systems

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