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Control Theory and Applications, IEE Proceedings -

Issue 5 • Date Sep 2000

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Displaying Results 1 - 9 of 9
  • Unbiased identification of stochastic linear systems subject to coloured noise

    Page(s): 485 - 491
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (492 KB)  

    Parameter estimation of transfer function models in the case of coloured process noise is studied. A new bias correction based method is proposed with a view to attaining estimation consistency irrespective of noise dynamics. A new vector of transfer function parameters is introduced, accompanied by a new data regression vector. The special structure of the new parameter vector enables calculation of the coloured-noise-induced bias, which can eventually result in unbiased parameter estimates via the bias correction scheme. It is demonstrated that the proposed method belongs to the family of the weighted instrumental variable methods while it also presents a simple yet efficient technique to compose a special type of instruments. The theoretical analysis is supported by numerical results View full abstract»

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  • Actuator fault estimation with disturbance decoupling

    Page(s): 501 - 508
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (480 KB)  

    The paper deals with actuator fault estimation with disturbance decoupling that makes it possible to diagnose actuator faults. To achieve reliable fault estimation, unknown input decoupling is combined with an observer, leading to a diagnostic observer. As a result, the observation error equation for the diagnostic observer does not depend on the effect of unknown inputs and is used to construct a diagnostic algorithm that provides an estimate of the gains of actuators; this can be obtained directly via the augmented error technique. This approach is compared with the design previously reported in the simulation. The simulation results indicate that the proposed algorithm is more realistic, in the sense that better decoupling properties can be assured without knowledge about unknown inputs, and it is potentially useful in the development of a fault-tolerant control system View full abstract»

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  • Implementation of the Takagi-Sugeno model-based fuzzy control using an adaptive gain controller

    Page(s): 509 - 514
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    A fuzzy controller of the type of the Takagi-Sugeno model is investigated. The proposed adaptive gain controller for Takagi-Sugeno fuzzy control, which results from the direct adaptive approach, is employed to directly adapt the appended gain parameters in the THEN-part of the Takagi-Sugeno model. Then, the constructed Takagi-Sugeno fuzzy controller can be best approximated to a given optimal control. In this study, the THEN-part of each rule with only an injected parameter can be adapted to determine the control action of each rule. The best merits of the proposed method are that the number of regulated parameters depends on the number of rules, and the design algorithm is implemented more easily than existing approaches. In addition, a maximum control is established to guarantee the system robust stability. The derivation shows that the proposed controller is stable in the sense of Lyapunov. Finally, a nonlinear system simulation example is applied to verify the effectiveness and ability of the proposed controller View full abstract»

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  • Robot angular link velocity estimation in the presence of high-level mixed uncertainties

    Page(s): 515 - 522
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    The estimation problem of the angular link velocity via a nonlinear high-gain observer is considered. It is solved in the presence of essential parametric uncertainty and external noise perturbations based only on current position measurements. The obtained state space estimation error turns out to be robust with respect to given uncertainties, and its bound can be expressed by a linear combination of the internal (parametric) and external (noise type) uncertainty levels. The simulation results, concerning a robot manipulator with two degrees of mobility and with unknown friction parameter subjected to variations within a given interval, illustrate the effectiveness of the suggested technique View full abstract»

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  • Adaptive control of a solar power plant using a multi-model

    Page(s): 493 - 500
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (576 KB)  

    The paper shows the application of indirect adaptive control (LQG controller) to the distributed collector field of a solar power plant at the Plataforma Solar de Almeria (Spain). The region of operation is split up into five operating-points which are represented by five different dynamical linear third-order ARMAX-models to describe the plant characteristics. The actual operating-point of the plant is determined by a characteristic value combining several measurements. The algorithm contains an online identification procedure to determine and to update the respective model of the operating-point. If the operating conditions change slowly a soft transition between the different operating-points is carried out View full abstract»

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  • Multivariable generalised predictive controller based on the Smith predictor

    Page(s): 538 - 546
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (764 KB)  

    A multivariable model-based predictive algorithm based on the Smith predictor can be used to control open-loop stable plants. By using the Smith predictor instead of the optimal predictor, the new controller increases robustness of the multivariable generalised predictive controller while maintaining nominal performance. Treatment of the multi-delay case is straightforward and the design of filters to improve robustness is simple. To illustrate the properties of the controller a simulation case study of the control of a distillation column is presented View full abstract»

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  • Plant-wide predictive control for a thermal power plant based on a physical plant model

    Page(s): 523 - 537
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1068 KB)  

    A constrained nonlinear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller. One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order nonlinear plant model, simulating the dominant characteristics of a 200 MW oil-fired power plant has been used to test the NPMPC algorithm. The results compare favourably to those obtained with the state-space GPC method designed under similar conditions View full abstract»

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  • Integrated approach to observer eigenstructure assignment

    Page(s): 556 - 560
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    An integrated approach to eigenstructure assignment is used to provide a simple unconstrained algorithm for full-state-observer left-eigenstructure assignment. The integrated approach enables the left-eigenstructure, assignable with multiple eigenvalues, to be readily identified. The approach is developed to provide a simple unconstrained algorithm for arbitrary reduced-order observer left-eigenstructure assignment when the number of system outputs equals or exceeds half the number of state variables View full abstract»

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  • Frequency response of uncertain systems: a 2q-convex parpolygonal approach

    Page(s): 547 - 555
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    Deals with the problem of computing the frequency response of an uncertain transfer function whose numerator and denominator polynomials are independent uncertain polynomials of the form P(s, q)=a0(q)+a1(q)s+...+an(q)sn, whose coefficients depend linearly on q=[p1, p2, ..., pq]T, and the uncertainty box is Q={q:pi ∈[p_i_, p¯i¯], i=1, 2, ..., q}. Using the geometric structure of the value set of P(s, q), powerful procedures are developed for computing the frequency response of these uncertain systems. A feature of the approach is the use of the 2q-convex parpolygonal value set of P(s, Q) and transition frequency concept. Thus, the approach eliminates some exposed edges of the corresponding polytopes of the numerator and denominator polynomials which are not useful for construction of the Bode, Nyquist, and Nichols envelopes. These results are used for computing robust gain and phase margins, and to design robust controllers for systems with affine linear uncertainty. Examples illustrate the benefit of the presented method View full abstract»

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