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Control Theory and Applications, IEE Proceedings -

Issue 1 • Date Jan 1999

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Displaying Results 1 - 15 of 15
  • Genetic approach to decentralised PI controller tuning for multivariable processes

    Page(s): 58 - 64
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (748 KB)  

    A method for the tuning of decentralised PI controllers for multivariable processes, based on genetic algorithms is presented. The power capabilities of genetic algorithms in locating the optimal solutions to a given optimisation problem are exploited by determining the parameters of the PI controllers to meet specified performance objectives. The designer has the freedom to explicitly specify the required performance objectives for a given problem in terms of time-domain bounds on the closed-loop responses. Each set of controller parameters is directly evaluated by simulating the closed-loop system and comparing the resulting responses with the desired using a specially formulated objective function. The proposed method has the flexibility to be applicable to a wide range of multivariable processes. Simulation results illustrate the effectiveness of the proposed method. The choice of genetic algorithms as a suitable optimisation method is supported by comparing them with two conventional optimisation methods View full abstract»

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  • Identification of a dynamical system with input nonlinearity

    Page(s): 41 - 51
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (788 KB)  

    A novel identification approach based on an input-holding scheme is proposed for a class of nonlinear system which consists of a nonlinear element followed by a linear dynamical system. By only using an observed system output where the test input signal is held during a multiple of the output sampling interval, the parameters of the transfer function of the linear part can be identified first. Then the intermediate input between the nonlinear part and the linear part is reconstructed by using the estimated parameters of the linear part. Finally, the nonlinear element is identified by using the test signal and the reconstructed intermediate input. It is clarified that the consistency of the parameter estimates of the linear part is assured under some specified assumptions. The effectiveness of the proposed algorithm is examined in identification of a Hammerstein system which includes an unknown continuous and/or discontinuous nonlinearity such as a backlash View full abstract»

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  • Multiharmonic perturbations for nonparametric autotuning

    Page(s): 1 - 8
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    The improvement of a recently developed method for closed loop nonparametric system identification using multiharmonic perturbation signals is described. Intended primarily as a method for the periodic online retuning of three-term controllers, the original method is shown to benefit from an improved selection of its design parameters. A brief review of the original algorithm is first given, followed by discussion of the improved parameter selection and an illustration of its benefits, with the aid of a typical numerical example. The result is a computationally efficient algorithm enabling nonparametric estimation of the process around the critical design frequency of interest, while also maintaining minimal disruption to the process output during the identification phase. Periodic online retuning of the controller is then possible without the need for any further relay tests beyond its initial commissioning View full abstract»

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  • Automatic design of fuzzy rule base for modelling and control using evolutionary programming

    Page(s): 9 - 16
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (920 KB)  

    In designing a fuzzy model and a fuzzy controller, we encounter a major difficulty in the identification of an optimised fuzzy rule base, which is traditionally achieved by a tedious trial and error process. The paper presents an approach to automatic design of optimal fuzzy rule bases for modelling and control using evolutionary programming. Such programming simultaneously evolves the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, five examples for modelling and control are examined. The performance of the identified fuzzy models and fuzzy controllers is demonstrated View full abstract»

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  • Stability robustness of feedback linearisable systems with input unmodelled dynamics

    Page(s): 77 - 83
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    The stability robustness of a feedback linearisable system subject to unstructured uncertainty is studied, with respect to the concepts of local input-to-state stability (ISS), local input-to-output stability (IOS) and small-signal L2-stability. A nonlinear small-gain theorem for feedback interconnected systems that are characterised by both local ISS and IOS is established. This theorem is used to derive sufficient conditions for robust local ISS and IOS of the closed-loop system that is produced by the application of feedback linearising control to a nonlinear feedback linearisable system with unmodelled dynamics. Moreover, sufficient conditions for robust small-signal L2-stability are derived by employing both the aforementioned nonlinear small-gain theorem and the well known small-gain theorem for L 2-stability. The proposed are easily verifiable. A nontrivial illustrative example is included View full abstract»

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  • Use of a recurrent neural network in discrete sliding-mode control

    Page(s): 84 - 90
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (592 KB)  

    Discusses a class of nonlinear discrete sliding-mode control. The control system is designed on the basis of a discrete Lyapunov function. Part of the equivalent control is estimated by an online estimator, which is realised by a recurrent neural network (RNN) because of its outstanding ability for modelling a dynamical process. A real-time iterative learning algorithm is developed and used to train the RNN. Unlike the conventional learning algorithms for RNNs, the proposed algorithm ensures that the learning error converges to zero. As a result, the stability of the control system is always assured. In addition, this learning algorithm can be applied for online estimation. The proposed controller eliminates chattering and provides sliding-mode motion on the selected manifolds in the state space. Numerical examples are given and simulation results strongly demonstrate that the control scheme is very effective View full abstract»

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  • Robust linearising controllers for nonlinear time-delay systems

    Page(s): 91 - 97
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    A salient feature of robust control schemes for nonlinear systems with delay on the state and the input is addressed. Using a state delay-dependent feedback linearisation algorithm, a nonlinear state-delay system having a linear input-output map can be determined. Composed of the Smith-type prediction and feedback linearisation, the nominal time-delay system can be completely compensated. An extended Lyapunov-based control technique is integrated into the linearising controller as prediction such that the uncertain time-delay system can be stabilised and the tracking trajectory will have uniform ultimate boundedness. Finally, a chemical reactor with delays in the recycle stream and manipulated input is demonstrated View full abstract»

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  • Indirect adaptive control via parallel dynamic neural networks

    Page(s): 25 - 30
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (432 KB)  

    Stability conditions for a parallel dynamic neural network by means of Lyapunov-like analysis are determined. The new learning law ensures that the identification error converges to zero (model matching) or to a bounded zone (with unmodelled dynamics). Based on the neural identifier we present a local optimal controller and analyse the tracking error. Our principal contributions are that we provide a bound for the identification error of the parallel neuro identifier and that we then establish a bound for the tracking error of the neurocontrol View full abstract»

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  • Eigenstructure assignment by output feedback: the case of common open- and closed-loop characteristic vectors

    Page(s): 37 - 40
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (260 KB)  

    A simple proof of an algorithm for eigenstructure assignment by output feedback is presented that allows naturally for the assignment of common open- and closed-loop characteristic vectors. The result is significant because the number of outputs plus the number of inputs required for arbitrary eigenvalue assignment may be reduced when some of the desired closed-loop characteristic vectors are common to the open-loop system. This is demonstrated by example View full abstract»

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  • Use of frozen-time H controllers in time-varying applications

    Page(s): 31 - 36
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    This paper considers the use of frozen-time H optimal controllers for time-varying applications. It is shown that, under suitable requirements on the plant, this method will result in an internally stable time-varying plant. This is done by first showing that the algebraic Riccati equations associated with the H control problem are analytic with respect to the plant data. Classical results of Desoer (1970) on the stability of frozen-time systems are then invoked View full abstract»

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  • Design method and application for fuzzy logical controller based on L Lyapunov functions

    Page(s): 17 - 24
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (628 KB)  

    A stability analysis and controller design for fuzzy logical systems described by the Takagi-Sugeno model is introduced, and this method is based on a common L Lyapunov function method. The fuzzy logical controller with a parallel distributed compensation structure is employed, and then the overall closed-loop system can be represented by a time-variant system. It is proved that the existence of the L Lyapunov function is the necessary and sufficient stability condition for the time-variant system. A stability criterion based on the L Lyapunov function is also discussed in the paper. An efficient iterative algorithm is developed to find the L Lyapunov functions for these control systems View full abstract»

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  • Exact feedback linearisation of a fifth-order model of synchronous generators

    Page(s): 53 - 57
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (436 KB)  

    In the last decade various control schemes for synchronous generators based upon exact feedback linearisation have been proposed. It is shown that a fifth-order model of synchronous machines can be exactly linearised via static state feedback. Such a model improves the modelling capability of the third-order model, which is commonly used when feedback-linearisation-based techniques are resorted to View full abstract»

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  • Continuous-time generalised predictive control of delay systems

    Page(s): 65 - 75
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (832 KB)  

    Continuous-time generalised predictive control (CGPC), equipped with the mechanism of anticipated filtering (AF) of control error, and tuned with the use of the idea of swiftness of system reaction (SSR), is considered in the context of the control of continuous-time delay systems. The basic AF-CGPC strategy is first deliberated, and afterwards several solutions for the delay-plant control design problem are developed, discussed in view of stability and robustness, and verified by means of simulation View full abstract»

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  • Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC-DC converter

    Page(s): 99 - 107
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (728 KB)  

    A fuzzy neural network (FNN) position controller is proposed to control the ultrasonic motor (USM) servo drive. The FNN controller is trained online using the proposed delta adaptation law. Moreover, a new driving circuit for the travelling-wave type ultrasonic motor (USM), which is a push-pull DC-DC power converter and a two-phase series-resonant inverter combination, is presented. First, the network structure, the online learning algorithm and the proof of convergence of the learning algorithm of the FNN are described. Next, the operating principles of the proposed driving circuit for the USM are described in detail. Then, the FNN position controller is implemented to control the USM drive to reduce the influence of parameter uncertainties and external disturbances. The effectiveness of the proposed driving circuit and FNN controller is demonstrated by some experimental results View full abstract»

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  • Design of output feedback controllers and output observers

    Page(s): 108 - 112
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    A new approach to the problem of eigenvalue assignment by output feedback is proposed. For an n-order linear dynamical system with m inputs and r outputs, the problem is reduced to a (n-m) state feedback control problem constrained by a simple system of linear algebraic equations. This solution is obtained for m+r⩾n+1. Otherwise, a new minimal-order dynamical observer is developed to provide the required (n-m-r+1)-order additional output vector. In both cases the output feedback design is greatly simplified. An illustrative example, with an application to flight control, demonstrates the implementation of the design procedure View full abstract»

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