Recent years has seen much progress in the theory and application of iterative learning control schemes for both linear and (classes of) nonlinear dynamics. In the case of the former, many algorithms based on minimizi...
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This paper addresses the problem of implementing predictive controllers for supervisory level control systems. In this configuration the manipulated variables calculated by the Predictive controller are used as comman...
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Electro Speckle Pattern Interferometry (ESPI) and Shearography for quantitative analysis of a crack inside of pipelines are described. Shearography is used widely for non-destructive inspection because of high sensiti...
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ISBN:
(纸本)7560317685
Electro Speckle Pattern Interferometry (ESPI) and Shearography for quantitative analysis of a crack inside of pipelines are described. Shearography is used widely for non-destructive inspection because of high sensitivity and simple interferometer. However, it is difficult to determine the defect size quantitatively because there are so many effect factors- shearing distance, shearing direction, induced load, and crack dept., which is depend on operator's skill. In this paper, a crack inside of pipeline is inspected with Shearography and ESPI. Firstly, the condition of these factors in Shearography is optimized for quantitative analysis and the size of crack is determined. And also, ESPI is used for determination of crack size quantitatively also. This method is independent on information of a crack and only induced load play an important role. The deformation is measured three-dimensionally and differentiated for finding of strain concentration points, which is equal to result of Shearography so that crack size can be determined quantitatively without any information of crack.
A learning feedforward controller (LFFC) using the Bartlet window function is proposed for a better tracking control of linear system over a finite time interval. LFFC is applied as a feedforward controller to the exi...
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A learning feedforward controller (LFFC) using the Bartlet window function is proposed for a better tracking control of linear system over a finite time interval. LFFC is applied as a feedforward controller to the existing feedback controller. This paper demonstrates that using a simple window function-Bartlet (Fejer or triangular) window in signal processing, the design of a learning feedforward controller reduces to determining only two design parameters: the learning gain and the number of point in the window. Convergence analysis is presented together with a design procedure.
This paper examines two classes of algorithms that estimate a continuous time ARX type of models from discrete data: one is based on infinite impulse response (IIR) filters while the other is based on finite impulse r...
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This paper examines two classes of algorithms that estimate a continuous time ARX type of models from discrete data: one is based on infinite impulse response (IIR) filters while the other is based on finite impulse response (FIR) filters. The IIR filters use continuous time state variable filters, and discretisation is performed on the filtered derivatives. In contrast, the FIR filters are in a discrete form with carefully chosen coefficients to approximate the derivatives of the continuous time variables. The strength and weakness of each approach are discussed and demonstrated by a set of simulation examples.
Automatic acquisition of an object using model appearance from an environment is proposed in this paper. Robots directly interact with a defined environment in order to extract object shape from its scene. The robot e...
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ISBN:
(纸本)4907764197
Automatic acquisition of an object using model appearance from an environment is proposed in this paper. Robots directly interact with a defined environment in order to extract object shape from its scene. The robot extracts the targeted object's appearance, creating an eigenspace, and stores it into the memory server (or intelligent data carrier). Eigenspace is constructed every time a new object appears, and various appearances are accumulated gradually. A closed sequence of appearances is generated from the accumulated shapes, which is used for object recognition. Experimental results of object accumulation and recognition show the effectiveness of the proposed method.
Differential linear repetitive processes are a distinct class of 2D continuous-discrete linear systems of both applications and systems theoretic interest. In the latter area, they arise, for example, in the analysis ...
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Differential linear repetitive processes are a distinct class of 2D continuous-discrete linear systems of both applications and systems theoretic interest. In the latter area, they arise, for example, in the analysis of both iterative learning control schemes and iterative algorithms for computing the solutions of nonlinear dynamic optimal control algorithms based on the maximum principle. Repetitive processes cannot be analysed/controlled by direct application of existing systems theory and to date there are few results on the specification and design of control schemes for them. The paper uses an LMI setting to develop the first really significant results in this problem domain.
A paper deals with application of stochastic methods for dynamic neural network training. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of a f...
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A paper deals with application of stochastic methods for dynamic neural network training. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of a fault diagnosis system to generate residuals. Up-to-date training algorithms, based on the classical back propagation, suffer from entrapment in local minima of an error function. Two stochastic algorithms are tested as training algorithms to overcome these difficulties. Efficiency of the proposed learning methods is checked using data recorded at Lublin Sugar Factory, Poland.
This paper addresses the problem of implementing predictive controllers for supervisory level control systems. In this configuration the manipulated variables calculated by the Predictive controller are used as comman...
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This paper addresses the problem of implementing predictive controllers for supervisory level control systems. In this configuration the manipulated variables calculated by the Predictive controller are used as command signals for the Distributed control Systems, which provide references to the operator-tuned local PID controllers that act on the physical system. This structure introduces the problem of loosing of performance if the inner-loop controllers are re-tuned. The paper discusses the solution to this problem based on the use of a two-degrees-of-freedom structure in the inner loop, that separates open and closed-loop properties. Both design guidelines and robustness issues are discussed.
Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obst...
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Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a vision-guided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for self-stabilized flight, and report results on vision-based autonomous flights of duration exceeding ten minutes.
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