We investigate the fault tolerant control problem and propose an intelligent online sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffer...
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We investigate the fault tolerant control problem and propose an intelligent online sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffering from catastrophic faults or incipient failures. The approach is to continuously monitor the system performance and identify the system's current state by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. Meanwhile, an artificial neural network is initialized and compensates for the unknown fault dynamics online. When the online learning process converges, the control input is tuned again by using the output of the identification model and a new least upper bound for the remaining uncertainty is estimated to further reduce the tracking error.
The validation of sensor measurements has become an integral part of the operation and control of modern industrial equipment. The sensor under a harsh environment must be shown to consistently provide the correct mea...
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The validation of sensor measurements has become an integral part of the operation and control of modern industrial equipment. The sensor under a harsh environment must be shown to consistently provide the correct measurements. Analysis of the validation hardware or software should trigger an alarm when the sensor signals deviate appreciably from the correct values. Neural network based models can be used to estimate critical sensor values when neighboring sensor measurements are used as inputs. The discrepancy between the measured and predicted sensor values may then be used as an indicator for sensor health. The proposed winner take all experts (WTAE) network is based on a 'divide and conquer' strategy. It employs a growing fuzzy clustering algorithm to divide a complicated problem into a series of simpler sub-problems and assigns an expert to each of them locally. After the sensor approximation, the outputs from the estimator and the real sensor value are compared both in the time domain and the frequency domain. Three fault indicators are used to provide analytical redundancy to detect the sensor failure. In the decision stage, the intersection of three fuzzy sets accomplishes a decision level fusion, which indicates the confidence level of the sensor health. Two data sets, the Spectra Quest Machinery Fault Simulator data set and the Westland vibration data set, were used in simulations to demonstrate the performance of the proposed WTAE network. The simulation results show the proposed WTAE is competitive with or even superior to the existing approaches.
As dynamic systems become more complex, experience more rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challeng...
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As dynamic systems become more complex, experience more rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. We investigate the fault tolerant control problem and propose an intelligent sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffering from catastrophic faults or incipient failures. The approach is to continuously monitor the system performance and identify what the system's current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal by adding a robust term to confine the system performance within a boundary layer. At the same time, an artificial neural network is initialized and compensates for the unknown fault dynamics online. Once the online learning process converges, the control input is tuned again by using the output of the identification model and a new least upper bound for the remaining uncertainty is estimated to further reduce the tracking error. The simulation results show a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.
Vibration isolation control system design for a microgravity experiment mount is considered. The controller design based on dynamic sliding manifold (DSM) technique is proposed to attenuate the accelerations transmitt...
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A problem of sliding mode control design for a SISO nonlinear system with unmodeled actuator of the 2nd order is considered. A design methodology based on nonlinear dynamic sliding manifold (NDSM) is proposed to atten...
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We seek to build CIS research systems within a flexible, open architecture. In this paper, we outline our solutions to the problems of system design, construction, and integration in this environment: building distrib...
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A resonant filter approach is proposed for direct identification of continuous-time transfer functions from input-output data when the input contains periodic components. The asymptotic properties of the method are an...
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A resonant filter approach is proposed for direct identification of continuous-time transfer functions from input-output data when the input contains periodic components. The asymptotic properties of the method are analysed; in particular the noise reduction properties are emphasised. Some illustrative simulations are provided.
The emergence of intelligent control has seen a focus of attention on the ideas of learning control. This paper introduces the current state of the art in the area of iterative learning control (ILC). Together with a ...
The emergence of intelligent control has seen a focus of attention on the ideas of learning control. This paper introduces the current state of the art in the area of iterative learning control (ILC). Together with a general description of the problems that must be addressed, the paper explores the relationship between the performance of learning algorithms and the structure of the system to be controlled. The importance of system's relative degree (pole-zero excess) and the system's zeros are described and the role of prediction in improving performance is stated.
Repetitive processes are a distinct class of 2D systems of both practical and theoretical interest. We use work in behavioral theory for nD linear systems to characterize poles for the case of so-called discrete linea...
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ISBN:
(纸本)0780366387
Repetitive processes are a distinct class of 2D systems of both practical and theoretical interest. We use work in behavioral theory for nD linear systems to characterize poles for the case of so-called discrete linear repetitive processes. A unique feature is that the resulting poles lead to a physically based interpretation of stability for these processes.
Currently, remote interactions between patient and surgeon/ doctor are limited to visual and audio aspects only. The general objective of the research presented in this paper is to explore force feedback teleoperation...
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