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Control Systems Technology, IEEE Transactions on

Issue 5 • Date Sep 1998

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Displaying Results 1 - 9 of 9
  • A modular methodology for fast fault detection and classification in power systems

    Page(s): 623 - 634
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (176 KB)  

    This paper presents a modular yet integrated approach to the problem of fast fault detection and classification. Although the specific application example studied here is a power system, the method would be applicable to arbitrary dynamic systems. The approach is quite flexible in the sense that it can be model-based or model-free. In the model-free case, we emphasize the use of concepts from signal processing and wavelet theory to create fast and sensitive fault indicators. If a model is available then conventionally generated residuals can serve as fault indicators. The indicators can then be analyzed by standard statistical hypothesis testing or by artificial neural networks to create intelligent decision rules. After a detection, the fault indicator is processed by a Kohonen network to classify the fault. The approach described here is expected to be of wide applicability. Results of computer experiments with simulated faulty transmission lines are included View full abstract»

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  • Air-to-fuel ratio control of spark ignition engines using Gaussian network sliding control

    Page(s): 678 - 687
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB)  

    This paper treats air-to-fuel ratio control of a spark ignition engine. A direct adaptive control method using Gaussian neural networks is developed to compensate transient fueling dynamics and the measurement bias of mass air flow rate into the manifold. The transient fueling compensation method is coupled with a dynamic sliding mode control technique that governs fueling rate when the throttle change is not rapid. The proposed controller is simple enough for online computation and is successfully implemented on an automotive engine having a multiport fuel injection system View full abstract»

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  • Design and implementation of an adaptive dispatching controller for elevator systems during uppeak traffic

    Page(s): 635 - 650
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB)  

    We design a dispatching controller for elevator systems during uppeak passenger traffic with the ability to adapt to changing operating conditions. The design of this controller is motivated by our previous paper (1997) where we proved that for a queuing model of the uppeak dispatching problem a threshold policy is optimal (in the sense of minimizing the average passenger waiting time) with threshold parameters that depend on the passenger arrival rate. The controller, which we call the concurrent estimation dispatching algorithm (CEDA), uses concurrent estimation techniques for discrete-event systems. The CEDA allows us to observe the elevator system while it operates under some arbitrary thresholds, and concurrently estimate, in an unobtrusive way, what the waiting time would have been had the system operated under a set of different thresholds. These concurrently estimated waiting times are used to adapt the operating thresholds to match the elevator service rate to a changing passenger arrival rate. Implementation issues relating to the limited state information provided by actual elevator systems are resolved in a way that maintains modest computational requirements and avoids the need for supplemental sensors beyond those already typically provided. Numerical performance results show the advantages of the CEDA over currently used dispatching algorithms for uppeak View full abstract»

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  • Frequency-domain identification of gas turbine dynamics

    Page(s): 651 - 662
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (220 KB)  

    The identification of the fuel flow to shaft speed dynamics of a twin-shaft gas turbine is addressed, with the aim of validating thermodynamic engine models. A measured input signal must be used in estimation in order to exclude the fuel feed dynamics from the model. This has been shown to present problems when fitting discrete models to engine data, and this paper examines the direct estimation of s-domain models in the frequency domain. A number of different multisine test signals were applied to the engine for the purposes of model estimation and nonlinear detection. The use of frequency-domain techniques is shown to produce high-quality models, and the tests also yield information on the levels of noise and nonlinearity and the length of the pure time delay. This work illustrates the potential of frequency-domain techniques for modeling systems where a physical interpretation is to be made of the model and where the need for accuracy requires that a measured input signal be used in estimation View full abstract»

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  • Suppression of wing rock of slender delta wings using a single neuron controller

    Page(s): 671 - 677
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (164 KB)  

    Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit cycle roll oscillations at high angles of attack. A single neuron control scheme for suppression of wing rock of slender delta wings is presented. The effectiveness of the control scheme is demonstrated through software simulation and real-time control experiments in a wind tunnel on a 80° swept back wing. The suppression of the limit cycle is achieved in presence of control saturation. The robustness of the scheme is demonstrated by the suppression of the wing rock at various initial conditions and different angles of attack View full abstract»

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  • Kalman filters and neural-network schemes for sensor validation in flight control systems

    Page(s): 596 - 611
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    Detection, identification, and accommodation of sensor failures can be a challenging task for complex dynamic systems. This paper presents the comparison of two different approaches for the task of sensor failure detection, identification, and accommodation in a flight control system assumed to be without physical redundancy in the sensory capabilities. The first approach is based on the use of a set of online learning neural networks; the second approach is based on the use of a bank of Kalman filters. The objective is to evaluate the robustness of both schemes; the comparison is performed through testing of the schemes for several types of failures presenting different level of complexity in terms of detectability. The required computational effort for both schemes is also evaluated. For each of these failure types this comparison is performed at nominal conditions, that is with the system model and its noise perfectly modeled (with the Kalman filter scheme performing at nominal conditions), and at conditions, where discrepancies occur for the modeling of the system as well as the system and measurement noises. While the Kalman-filter-based scheme takes advantage of its robustness capabilities, the neural-network-based scheme, starting from a random numerical architecture, relies on the learning accumulated either online or from off-line simulations. The study reveals that online learning neural architectures have potential for online estimation purposes in a sensor validation scheme, particularly in the case of poorly modeled dynamics View full abstract»

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  • Sliding mode thermal control system for space station furnace facility

    Page(s): 612 - 622
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (276 KB)  

    This paper addresses the decoupled control of the nonlinear, multi-input-multi-output, highly coupled space station furnace facility (SSFF) thermal control system. Sliding mode control theory, a subset of variable-structure control theory, is employed to increase the performance, robustness, and reliability of the SSFF's currently designed control system. This paper presents the nonlinear thermal control system description and develops the sliding mode controllers that cause the interconnected subsystems to operate in their local sliding modes, resulting in control system invariance to plant uncertainties and external and interaction disturbances. The desired decoupled flow-rate tracking is achieved by optimization of the local linear sliding mode equations. The controllers are implemented digitally and extensive simulation results are presented to show the flow-rate tracking robustness and invariance to plant uncertainties, nonlinearities, external disturbances, and variations of the system pressure supplied to the controlled subsystems View full abstract»

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  • Repetitive control for the track-following servo system of an optical disk drive

    Page(s): 663 - 670
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB)  

    Disturbances acting on the track-following servo system of an optical disk drive inherently contain significant periodic components that cause tracking errors of a periodic nature. Such disturbances can be effectively rejected by employing a repetitive controller, which must be implemented carefully in consideration of system stability. Plant uncertainty makes it difficult to design a repetitive controller that will improve tracking performance yet preserve system stability. In this paper, we examine the problem of designing a repetitive controller for an optical disk drive track-following servo system with uncertain plant coefficients. We propose a graphical design technique based on the frequency domain analysis of linear interval systems. This design method results in a repetitive controller that will maintain system stability against all admissible plant uncertainties. We show simulation and experimental results to verify the validity of the proposed design method View full abstract»

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  • An aerodynamic moment-controlled surface for gust load alleviation on wind turbine rotors

    Page(s): 577 - 595
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (444 KB)  

    A method is described for limiting transient gust loading on horizontal-axis wind turbine rotors. The technique, known as aerodynamic moment control, is implemented by enclosing a pitchable section of the blade in an active control loop, using the external aerodynamic load as feedback variable. The actuator operates within an outer control loop, typically based on electrical power output. The properties of the actuator have been investigated by linear analysis, based on a constant-speed 330-kW wind turbine with active power control, and pitchable blade tips. Two cases were compared, in which the tip actuator was first implemented using position feedback (position control), then subsequently using aerodynamic moment feedback (moment control). The disturbance rejection properties of the overall power controller were found to improve in the latter case. A prototype aerodynamic moment controller has been demonstrated in wind tunnel tests. The controller was configured for an inherently unstable wing section, representing the pitchable tip of a wind turbine blade, at approximately 1/3 full scale. The response to external disturbances was investigated by introducing harmonic perturbations into the upstream airflow. The system successfully demonstrated the principle of aerodynamic moment feedback, although the actuator exhibited somewhat modest gust response characteristics due to the use of velocity feedback to enhance damping. The results of the tests, and the design implications for a full-scale wind turbine, are discussed View full abstract»

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