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Selected Abstracts

Central suboptimal H, filter design for nonlinear polynomial systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2009
Michael Basin
Abstract This paper presents the central finite-dimensional H, filter for nonlinear polynomial systems, which is suboptimal for a given threshold , with respect to a modified Bolza,Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original H, filtering problem to the corresponding optimal H2 filtering problem, using the technique proposed in (IEEE Trans. Automat. Control 1989; 34:831,847). The paper presents the central suboptimal H, filter for the general case of nonlinear polynomial systems based on the optimal H2 filter given in (Int. J. Robust Nonlinear Control 2006; 16:287,298). The central suboptimal H, filter is also derived in a closed finite-dimensional form for third (and less) degree polynomial system states. Numerical simulations are conducted to verify performance of the designed central suboptimal filter for nonlinear polynomial systems against the central suboptimal H, filter available for the corresponding linearized system. Copyright © 2008 John Wiley & Sons, Ltd. [source]

A new non-linear sliding-mode torque and flux control method for an induction machine incorporating a sliding-mode flux observer

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 5 2004
Fang Chen
Abstract In this paper a novel sliding-mode control algorithm, based on the differential geometry state-co-ordinates transformation method, is proposed to control motor torque directly. Non-linear feedback linearization theory is employed to decouple the control of rotor flux magnitude and motor torque. The advantages of this method are: (1) The rotor flux and the generated torque can be accurately controlled. (2) Robustness with respect to matched and mismatched uncertainties is obtained. Additionally, a varying continuous control term is proposed. As a result, chattering is eliminated without sacrificing robustness and precision. The control strategy is based on all motor states being available. In practice the rotor fluxes are not usually measurable, and a sliding-mode observer is derived to estimate the rotor flux. The observer is designed to possess invariant dynamic modes which can be assigned independently to achieve the desired performance. Furthermore, it can be shown that the observer is robust against model uncertainties and measurement noise. Simulation and practical results are presented to confirm the characteristics of the proposed control law and rotor flux observer. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Observer-based adaptive robust control of a class of nonlinear systems with dynamic uncertainties,

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4 2001
Bin Yao
Abstract In this paper, a discontinuous projection-based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi-strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time-history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal Form

ASIAN JOURNAL OF CONTROL, Issue 2 2001
J.Q. Gong
ABSTRACT In this paper, performance oriented control laws are synthesized for a class of single-input-single-output (SISO) n -th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat-able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on-line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy. [source]