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Control Theory and Applications, IEE Proceedings -

Issue 1 • Date 17 Jan. 2005

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Displaying Results 1 - 18 of 18
  • Editorial

    Page(s): 1 - 2
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  • Power system stabiliser for multimachine power system using robust decentralised periodic output feedback

    Page(s): 3 - 8
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (498 KB)  

    Power system stabilisers (PSSs) are added to excitation systems to enhance the damping during low-frequency oscillations. The design of decentralised robust PSS for four machines with ten buses using periodic output feedback is proposed. The nonlinear model of a multimachine power system is linearised at different operating points and 16 linear state-space models are obtained. For each of these models an output injection gain is obtained using LQR technique. A decentralised robust periodic output feedback gain which realises these output injection gains is obtained using the linear matrix inequality approach. This method does not require states of the system for feedback and is easily implemented. The robust decentralised periodic output control is applied to the nonlinear model of a multimachine system at different operating (equilibrium) points and gives encouraging results for the design of power system stabilisers. View full abstract»

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  • Stability analysis of discrete-time systems in a state-space realisation with state saturation nonlinearities: linear matrix inequality approach

    Page(s): 9 - 12
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (461 KB)  

    A computationally efficient linear matrix inequality (LMI)-based criterion for the global asymptotic stability of discrete-time systems in a state-space realisation with state saturation nonlinearities is presented. The criterion turns out to be an improved generalised version of a previously reported LMI-based criterion. The extension of the approach to a situation involving partial state saturation nonlinearities is performed. View full abstract»

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  • Iterative learning control for linear time-variant discrete systems based on 2-D system theory

    Page(s): 13 - 18
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (489 KB)  

    The two-dimensional (2-D) system theory iterative learning control (ILC) techniques for linear time-invariant discrete systems are extended to the cases of linear time-variant discrete systems. By exploiting the convergent property of 2-D linear time-variant discrete systems with only one independent variable, a kind of 2-D system theory ILC approach is presented for linear time-variant discrete systems. Sufficient conditions are given for convergence of the proposed ILC rules. Two numerical examples are used to validate the ILC procedures. View full abstract»

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  • Output feedback H/sub /spl infin// controller design for linear discrete-time systems with sensor nonlinearities

    Page(s): 19 - 26
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (524 KB)  

    The problem of output feedback H/sub /spl infin// control for linear discrete-time systems subject to sensor nonlinearities is considered. The existence conditions for the globally asymptotic stability and the H/sub /spl infin// performance for the closed-loop systems with sensor sector nonlinearities are derived. A method for designing the output feedback H/sub /spl infin// controller is then given within the framework of linear matrix inequalities. Based on these results, a controller design for the regional output feedback H/sub /spl infin// control problem with sensor saturation is proposed. Finally, an example is given to illustrate the effectiveness of the proposed methods. View full abstract»

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  • Delay-dependent robust H/sub /spl infin// control of uncertain linear systems with lumped delays

    Page(s): 27 - 33
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    A linear matrix inequality approach to the robust H/sub /spl infin// control problem of uncertain continuous- and discrete-time linear time-invariant systems with time delay in the state vector and control input is developed. The main results provide sufficient delay-dependent conditions for the control problem, where the explicit size of the time delay plays a crucial role for the closed-loop stability. The solutions that are found for the H/sub /spl infin// control problem are less conservative when compared with other approaches in the recent literature. View full abstract»

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  • Mathematical analysis of Westwood+TCP congestion control

    Page(s): 35 - 42
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (679 KB)  

    TCP congestion control is based on an additive-increase/multiplicative-decrease (AIMD) probing paradigm aimed at adapting the sending rate of TCP data sources to match the Internet time-varying available bandwidth. Westwood+ TCP has been recently proposed to improve the tracking of available bandwidth of classic TCP. It is based on an end-to-end estimate of the available bandwidth, which is obtained by properly counting and filtering the stream of acknowledgement packets. The estimate is used to adaptively decrease the congestion window and slow start threshold after congestion so that it can be said that Westwood+ TCP substitutes the classic multiplicative decrease with an adaptive decrease paradigm. The authors propose a mathematical analysis of the additive-increase/adaptive-decrease (AIADD) paradigm to analyse the steady-state throughput provided by Westwood+ TCP and investigate the intra-protocol fairness of the AIADD paradigm and the inter-protocol friendliness between AIADD and AIMD algorithms. It is shown that (i) both classic and Westwood+ TCP provide a throughput that is proportional to 1//spl radic/p, where p is the segment drop probability, that is they are friendly to each other; and (ii) the throughput of Westwood+ TCP is proportional to 1//spl radic/RTT, where RTT is the round trip time, whereas the throughput of Reno TCP is proportional to 1/RTT, i.e. Westwood+ TCP improves the intra-protocol fairness. Finally, Ns-2 simulations are reported in order to validate the mathematical model in the presence of a wide range of network loads, loss probabilities and round trip times. View full abstract»

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  • Optimal higher-order iterative learning control of discrete-time linear systems

    Page(s): 43 - 48
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (501 KB)  

    The optimal updating law of the problem of optimal higher-order iterative learning control is given explicitly and the convergence of the tracking errors is proved. Two examples show that the convergence of the optimal higher-order iterative learning control is faster than that of the optimal one-order iterative learning control. View full abstract»

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  • Disturbance decoupling of switched nonlinear systems

    Page(s): 49 - 54
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (499 KB)  

    The disturbance decoupling problem of switched nonlinear systems with arbitrary switching law is investigated by first considering the invariant and weak-invariant distributions of switched nonlinear systems. Based on the invariant distribution, the disturbance decoupling problem under two kinds of switching information is considered, using switch-dependent and switch-independent feedback. Sufficient conditions are obtained for solving the problem. An algorithm for the largest invariant distribution contained in the kernel of outputs is presented. View full abstract»

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  • Nonlinear filter design for fault diagnosis: application to the three-tank system

    Page(s): 55 - 64
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    The problem of fault detection and isolation in a nonlinear framework is addressed. A systematic method that ensures the synthesis of asymptotic nonlinear filters is proposed for residual generation. In order to show the effectiveness of the proposed approach, each theoretical step is illustrated by means of simulations. The example of a three-tank system is then experimentally considered to verify the validity of our approach. View full abstract»

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  • Neural-network-identification-based adaptive control of wing rock motions

    Page(s): 65 - 71
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (520 KB)  

    The proposed control system comprises a computation controller and a tracking controller. The computation controller containing a recurrent neural network identifier is the principal controller, and the tracking controller is designed to achieve L/sub 2/ tracking performance with a desired attenuation level. To investigate the effectiveness of the proposed control system the design methodology is used to control a wing rock motion, manifested by a limit-cycle oscillation predominantly for an aircraft operating at subsonic speeds and high angles of attack. Simulation results demonstrate that the proposed control system can achieve favourable tracking performance for the wing rock motion. View full abstract»

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  • An inversion-based iterative learning control algorithm for a class of nonminimum-phase systems

    Page(s): 72 - 78
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (511 KB)  

    In a new inversion-based iterative learning algorithm for a class of nonminimum-phase systems the least-squares method is used to estimate the system parameters after each repetitive trial. The output tracking error and the identified system model are used through stable inversion to find the feedforward input, together with the desired state trajectories, for the next trial. A robust controller is used in each trial to ensure the stability of the systems and the output tracking error convergence. Sufficient conditions for learning control convergence are provided. Simulation studies on systems with gain uncertainty and time constant uncertainty are also presented. Simulation results demonstrate that the proposed learning control scheme is effective in reproducing the desired trajectories. View full abstract»

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  • Inverse optimal control of nonlinear systems with structural uncertainty

    Page(s): 79 - 83
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (491 KB)  

    Inverse optimal control for nonlinear systems with structural uncertainty is considered. Based on the control Lyapunov function, a theorem for the globally asymptotic stability is presented. From this a less conservative condition for the inverse optimal control is derived. The result is used to design an inverse optimal controller for a class of nonlinear systems, that improves and extends the existing results. The class of nonlinear systems considered is also enlarged. The simulation results show the effectiveness of the method. View full abstract»

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  • Integral-equation-based continuous-time model identification of a magnetostrictive actuator

    Page(s): 85 - 89
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (498 KB)  

    The problem of the continuous-time model identification of a magnetostrictive actuator is considered. An integral-equation-based least squares method is proposed. The identification is based on the sampled data of an input-output measurement with high frequency coloured noise. The main idea of the method is to transform a differential equation into an integral equation so that the effect of the high-frequency coloured noise can be ignored. The method is successfully applied to parameter identification in a continuous-time model of a magnetostrictive actuator. View full abstract»

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  • Control of nonlinear singularly perturbed systems using feedback linearisation

    Page(s): 91 - 94
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (473 KB)  

    A systematic approach to the control of a class of nonlinear singularly perturbed systems using feedback linearisation is proposed. The /spl epsi/-independent diffeomorphism is engaged to transform the nonlinear systems into the linearised singularly perturbed form. Then the linear controller is robustly designed for the unknown /spl epsi/. The practical aspects of the proposed scheme are illustrated through the control of a series DC motor. View full abstract»

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  • Neural dynamic programming based online controller with a novel trim approach

    Page(s): 95 - 104
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (643 KB)  

    Neural dynamic programming (NDP) is a generic online learning control system based on the principle of reinforcement learning. Such a controller can self tune with a wide change of operating conditions and parametric variations. Implementation details of a self-tuning NDP based speed controller of a permanent-magnet DC machine along the online training algorithm are given. A simple solution is developed for finding the trim control position for the NDP controller NDP controller that can be extended to other problems. The DC machine is chosen for the implementation because it can be easily operated in a variety of operating conditions, including parametric variations, to prove the robustness of the controller and its multiobjective capabilities. The simulation results of the NDP controller are compared with the results of a conventional PI controller to access the overall performance. View full abstract»

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  • Flatness-based optimal noncausal output transitions for constrained nonlinear systems: case study on an isothermal continuously stirred tank reactor

    Page(s): 105 - 112
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (612 KB)  

    The issue of optimal output transition control for nonlinear differential flat systems with constraints is investigated. Of special interest is the generation of a state reference trajectory and a feedforward input, which are essential to the two-degree-of-freedom design. The proposed approach is to transfer the transition problem in the real output space into a trajectory planning issue in the flat output space. The distinguishing feature of our approach is the generation of a noncausal trajectory for the flat output. This approach is shown to be highly effective in creating performance improvements. It should also be noted that the proposed methodology guarantees that the planned trajectories are feasible for all nonlocal transitions. This allows the application of stable inversion in the planning of optimal output transitions. The proposed method is illustrated on a benchmark system, the isothermal continuous stirred tank reactor, although its applicability is much wider. View full abstract»

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  • LMI-based sliding-mode observer design method

    Page(s): 113 - 115
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    In an LMI-based sliding-mode observer design method for a class of multivariable uncertain systems the switching surface is set to be the difference between the observer and system output. In terms of LMIs, a necessary and sufficient condition is derived for the existence of a sliding-mode observer guaranteeing a stable sliding motion on the switching surface that is insensitive to matched uncertainties. The gain matrices of the sliding-mode observer are characterised using the solution of the LMI existence condition. The approach is direct and does not require state transformation, and has advantages in computation. View full abstract»

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