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Output Tracking (output + tracking)

Distribution by Scientific Domains
Engineering100%

Kinds of Output Tracking

  • asymptotic output tracking
    		•

    Selected Abstracts

    Adaptive robust control of nonlinear systems with dynamic uncertainties

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2009
    Xiangbin Liu
    Abstract In this paper, the discontinuous projection-based adaptive robust control (ARC) approach is extended to a class of nonlinear systems subjected to parametric uncertainties as well as all three types of nonlinear uncertainties,uncertainties could be state-dependent, time-dependent, and/or dynamic. Departing from the existing robust adaptive control approach, the proposed approach differentiates between dynamic uncertainties with and without known structural information. Specifically, adaptive robust observers are constructed to eliminate the effect of dynamic uncertainties with known structural information for an improved steady-state output tracking performance,asymptotic output tracking is achieved when the system is subjected to parametric uncertainties and dynamic uncertainties with known structural information only. In addition, dynamic normalization signals are introduced to construct ARC laws to deal with other uncertainties including dynamic uncertainties without known structural information not only for global stability but also for a guaranteed robust performance in general. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Reduced-order robust adaptive control design of uncertain SISO linear systems

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2008
    Qingrong Zhao
    Abstract In this paper, a stability and robustness preserving adaptive controller order-reduction method is developed for a class of uncertain linear systems affected by system and measurement noises. In this method, we immediately start the integrator backstepping procedure of the controller design without first stabilizing a filtered dynamics of the output. This relieves us from generating the reference trajectory for the filtered dynamics of the output and thus reducing the controller order by n, n being the dimension of the system state. The stability of the filtered dynamics is indirectly proved via an existing state signal. The trade-off for this order reduction is that the worst-case estimate for the expanded state vector has to be chosen as a suboptimal choice rather than the optimal choice. It is shown that the resulting reduced-order adaptive controller preserves the stability and robustness properties of the full-order adaptive controller in disturbance attenuation, boundedness of closed-loop signals, and output tracking. The proposed order-reduction scheme is also applied to a class of single-input single-output linear systems with partly measured disturbances. Two examples are presented to illustrate the performance of the reduced-order controller in this paper. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Stable robust feedback control system design for unstable plants with input constraints using robust right coprime factorization

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 18 2007
    Mingcong Deng
    Abstract A stable robust control system design problem for unstable plants with input constraints is considered using robust right coprime factorization of nonlinear operator. For obtaining strong stability of the closed-loop system of unstable plants with input constraints, a design scheme of robust nonhyphen-linear control system is given based on robust right coprime factorization. Some conditions for the robustness and system output tracking of the unstable plant with input constraints are derived. Numerical examples are given to demonstrate the validity of the theoretical results. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Robust inverse optimal control laws for nonlinear systems

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 15 2003
    Nael H. El-Farra
    Abstract This work proposes a robust inverse optimal controller design for a class of nonlinear systems with bounded, time-varying uncertain variables. The basic idea is that of re-shaping the scalar nonlinear gain of an LgV controller, based on Sontag's formula, so as to guarantee certain uncertainty attenuation properties in the closed-loop system. The proposed gain re-shaping is shown to yield a control law that enforces global boundedness of the closed-loop trajectories, robust asymptotic output tracking with an arbitrary degree of attenuation of the effect of uncertainty on the output, and inverse optimality with respect to a meaningful cost that penalizes the tracking error and the control action. The performance of the control law is illustrated through a simulation example and compared with other controller designs. Copyright © 2003 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]

    ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY

    ASIAN JOURNAL OF CONTROL, Issue 4 2004
    Ali J. Koshkouei
    ABSTRACT This paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi-strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty. [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]