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Control Theory and Applications, IEE Proceedings -

Issue 5 • Date Sep 1995

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Displaying Results 1 - 16 of 16
  • Adaptive control of dominant time delay systems via polynomial identification

    Page(s): 433 - 438
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    An adaptive algorithm for controlling systems with dominant time delay is presented. The algorithm is derived by integration of Hagglund's predictive PI (PIP) controller with the online polynomial identification algorithm. For the purpose of identification and control, the system under control is modelled by a first-order with delay model and its parameters, including the time delay, are identified. The identified parameters are used to tune the predictive PI controller. The performance of this algorithm is verified by an experimental study for a system with variable time delay. It is also shown that this controller performs better than the popular Foxboro's EXACT controller for a dominant time delay process View full abstract»

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  • Redesign of robust controllers for rigid-link flexible-joint robotic manipulators actuated with harmonic drive gearing

    Page(s): 508 - 514
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (516 KB)  

    The authors have redesigned a previously developed robust tracking controller for rigid-link flexible-joint (RLFJ) robot manipulators to handle flexibilities specifically induced by harmonic drive gearing. A more realistic, and consequently more complex model for the torque transmission dynamics which includes frictional losses, kinematic error and nonlinear compliance is utilised. The stability result achieved for the authors' proposed controller ensures that the link tracking error is `globally uniformly ultimately bounded' (GUUB), in spite of additive bounded disturbances, parametric uncertainty, and the presence of complex nonnegligible actuator dynamics View full abstract»

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  • Network-growth approach to design of feedforward neural networks

    Page(s): 486 - 492
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (488 KB)  

    A critical issue in applying the multilayer feedforward networks is the need to predetermine an appropriate network size for the problem being solved. A network-growth approach is pursued to address the problems concurrently and a progressive-training (PT) algorithm is proposed. The algorithm starts training with a one-hidden-node network and a one-pattern training subset. The training subset is then expanded by including one more pattern and the previously trained network, with or without a new hidden node grown, is trained again to cater for the new pattern. Such a process continues until all the available training patterns have been taken into account. At each training stage, convergence is guaranteed and at most one hidden node is added to the previously trained network. Thus the PT algorithm is guaranteed to converge to a finite-size network with a global minimum solution View full abstract»

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  • Variable structure based decentralised adaptive control

    Page(s): 439 - 443
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (284 KB)  

    A new scheme for decentralised adaptive control is proposed. This scheme is based on a variable structure adaptive controller and a proportional controller. The adaptive variable structure component of this scheme is used to compensate uncertain interconnections among the subsystems and to ensure global stability of the overall system. The design of the adaptive controller is totally model free. The simulation results are also presented to demonstrate the performance of the closed loop control system View full abstract»

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  • Variable-speed drives incorporating interacting multiloop adaptive controllers

    Page(s): 411 - 419
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (552 KB)  

    A detailed design procedure for multiloop adaptive controllers for a DC drive is presented. Details of the main features of a partitioned-estimator identification scheme, the controller structure and the pole-placement design procedure are discussed. Results are presented for both twin- and triple-loop adaptive controller configurations. Drive responses obtained with the twin-loop configuration are compared to the corresponding performance obtained with fixed gain controllers. The effects of sampling interval, tuning start-up delay, controller and estimator initialisation and drive parameter variation are discussed, and recommendations are made. The basic transient response of the drive is shown to be insensitive to changes in operating conditions and drive parameters. An important feature is that the adaptive controllers can be implemented from an initial open loop configuration thereby eliminating the need for any detailed a priori design View full abstract»

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  • Efficient pole placement technique for linear time-variant SISO systems

    Page(s): 451 - 458
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (452 KB)  

    The paper deals with the transformation of linear single-input/single-output (SISO) systems into Frobenius canonical form of which the ultimate objective is to achieve a new and computationally efficient methodology towards a desired pole placement. The classical formula of Ackermann is generalised for both time-invariant and time-varying systems as a result of this study. The advantage of the proposed technique is that it does not require the computation of characteristic polynomial coefficients or the eigenvalues of the original system, nor the coefficients of the characteristic polynomial of the transformed system. Its computational efficiency is demonstrated to be superior in comparison with some other commonly used techniques View full abstract»

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  • Polynomial solution of the standard H control problem for strip mill gauge control

    Page(s): 515 - 525
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (592 KB)  

    A solution to the standard H optimal control problem is presented which is particularly useful in machine control applications where the output to be controlled is different from the signal for feedback. The solution is obtained in polynomial form and the plant structure which is assumed represents a range of applications in the manufacturing and process industries. The results are applied to the design of a thickness-control system for cold-rolling mills subject to a range of disturbance noise inputs View full abstract»

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  • Neural-network-based near-time-optimal position control method for DC motor servosystems

    Page(s): 493 - 500
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (536 KB)  

    The paper considers the development and implementation of a near-time-optimal neural-network-based position control method for DC motor servosystems. To bypass the difficulties caused by system constraints and modelling uncertainties, the paper uses classification neural networks to learn the time-optimal control law from experimentally generated near-time-optimal trajectories. In addition, by using regression neural networks to learn the relationship between control object displacement and the armature voltage pulse-width, a variable-pulsewidth control strategy is developed to achieve accurate positioning. Experimental results are given to demonstrate the effectiveness of the proposed approach View full abstract»

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  • Robust control of series parallel resonant converters

    Page(s): 401 - 410
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (448 KB)  

    A robust controller is designed for the series parallel resonant DC/DC converter based on the linear quadratic Gaussian/loop transfer recovery (LQG/LTR) methodology. The controller structure, comprising a servo-compensator with an internal model, reference state observer, plant and disturbance state observer, ensures tracking of the reference voltage and rejection of system disturbances. The controller performance is insensitive to converter parametric and operational variations. The controller design is based on a converter small-signal model derived using the principles of the describing function and harmonic balance on the nonlinear time-varying converter equations. The controlled converter is shown by computer simulation to perform excellently well, with very good tracking and disturbance capabilities in the presence of changes of load and input voltage View full abstract»

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  • Functional diagnosis and prescription of measurements using effort and flow variables

    Page(s): 420 - 432
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1052 KB)  

    The paper proposes an approach to functional reasoning for diagnosis, based on an ontology that is aimed at providing a more formal physical foundation to functional knowledge. The approach (called FDef, i.e. functional diagnosis with efforts and flows) is analysed from several perspectives: reasoning strategy, modelling, formalisation of the reasoning activities and of the entities involved, architecture, comparison with related work, main assumptions and limitations. Furthermore, the paper shows in detail how the minimum entropy approach to measurement prescription can be simplified and used in FDef. Several diagnostic examples are considered and discussed in the paper View full abstract»

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  • DSP based integral variable structure control for DC motor servo drivers

    Page(s): 444 - 450
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (292 KB)  

    The design and implementation of a DSP microprocessor based DC motor servo driver are presented. The integral variable structure control (IVSC) approach is proposed for the outer loop of the driven system. Conditions are derived which ensure the existence of a nonideal sliding motion for the IVSC approach and also prove that the described motion in the nonideal sliding region will be close to the ideal sliding motion. Simulation and experimental results show that the proposed scheme can achieve an accurate velocity/position servo tracking result and is robust to plant parameter variations and external load disturbance View full abstract»

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  • BPD computation and model reference adaptive control (MRAC) of Hammerstein plants

    Page(s): 475 - 485
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (668 KB)  

    A large class of control strategies relies on computation of the output gradient vector. Recently the concept of block partial derivatives (BPD) and the associated algebra have been introduced to facilitate the computation of the gradient vector in nonlinear systems. The derivation presented in that reference, however, cannot easily be applied to control plants with known structure. In this paper, we propose a simpler procedure for BPD computation. The proposed BPD computation procedure is then applied to derive MRAC schemes for Hammerstein plant. The choice of design parameters based on local stability are provided and results of simulation study are reported View full abstract»

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  • Adaptive control of discrete systems using neural networks

    Page(s): 526 - 528
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (132 KB)  

    Etxebarria (1994) has presented a simple adaptive control scheme for discrete systems using two linear two-layered neural networks. Specifically, one of these networks is used to learn online the dynamics of the unknown plant using the Widrow-Hoff delta rule. The other network uses this learning to adjust its connection weights and to generate the control signal. Etxebarria has proven that the resulting closed-loop system is globally stable and has shown that the controlled output tracks the reference signal asymptotically. The author states that there is room for improving the performance of the adaptive neural control scheme of Etxebarria, in particular, its transient performance. In this correspondence, a method is proposed to enhance the transient performance of the above control scheme by replacing the output y of the unknown plant by a linear combination of y and its derivative y˙. Furthermore, the proposed method does not change the structure of both the neural estimator and controller and, as such, the increase in overall computation is minimal. Two examples, based on simulation and experimental studies, are presented to demonstrate the effectiveness of the proposed method View full abstract»

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  • Model-based compensation and comparison of neural network controllers for uncertainties of robotic arms

    Page(s): 501 - 507
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    The paper proposes a decentralised compensation scheme for unstructured uncertainties and modelling errors of robotic manipulators. The scheme employs a central decoupler and independent joint neural network controllers. Recursive Newton Euler formulae are used to decouple robot dynamics to obtain a set of equations in terms of the input and output of each joint. To identify and suppress the effects of uncertainties associated with the model, each joint is controlled separately by neural network controllers. Gaussian radial basis neural networks, using the direct adaptive technique for weight updates, and multilayered perceptrons, using the backpropagation learning algorithm, are used as the adaptive elements in the control scheme. The effectiveness of the proposed scheme is demonstrated by controlling the trajectories of the three primary joints of a PUMA 560. Simulation results show that this control scheme can achieve fast and precise robot motion control under substantial model inaccuracies. Properties of both types of compensators are compared with conventional adaptive control, and suitability for real-time control is discussed View full abstract»

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  • Digital control system design for a unique nonlinear MIMO process using QFT technique

    Page(s): 466 - 474
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (584 KB)  

    A 19-input, 106-output thermal process is closed-loop-stabilised to meet time- and frequency-domain performance criteria. A unique nonlinear process modelling technique is used to transform several nonmeasurable process parameters into two `state-dependent linear variables' (gain and dominant pole location) with quantified uncertainty. A linear equivalent model set with quantified uncertainty is amenable to the quantitative feedback theory (QFT) design technique, and the analogue multiloop compensation was developed using QFT. Because the system primarily functions as a regulator, a simplified MIMO system decoupling method, specific to this type of process, was developed and demonstrated. The resulting digital control system is in operation on the Uranium Atomic Vapor Laser Isotope Separation Demonstration System at the Lawrence Livermore National Laboratory View full abstract»

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  • Centralised integration of multisensor noisy and fuzzy data

    Page(s): 459 - 465
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    The paper discusses centralised integration of multisensor noisy and fuzzy data, which employs both Kalman filtering and fuzzy arithmetic. Because of the property of fuzzy arithmetic, the parameter fuzziness in a system under extended operation will increase without limit and finally reach an unacceptable range. Hong and Wang adopted a new compression technique to solve this problem. This paper extends their work on single sensor noisy and fuzzy data filtering to multisensor noisy and fuzzy data filtering. An example is given to illustrate the effectiveness of the algorithm presented View full abstract»

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