58 research outputs found

    Robust Adaptive Control in H(infinity).

    Get PDF
    This dissertation addresses the problem of unifying identification and control in the paradigm of {\cal H}\sb\infty to achieve robust adaptive control. To achieve robust adaptive control, we employ the same approach used for identification in {\cal H}\sb\infty and robust control in {\cal H}\sb\infty. In the modeling part, we aim not only to identify the nominal plant, but also to quantify the modeling error in {\cal H}\sb\infty norm. The linear algorithm based on least-squares is used, and the upper bounds for the corresponding modeling error are derived. In the control part, we aim to achieve the performance specification in frequency domain using innovative model reference control. New algorithms are derived that minimize an {\cal H}\sb\infty index function associated with the deviation between the performance of the feedback system to be designed, and that of the reference model. The results for the modeling and control part are then combined and applied to adaptive control. It is shown that with mild assumption on persistent excitation, the least squares algorithm in frequency domain is equivalent to the recursive least squares algorithm in time domain. Moreover, finite horizon {\cal H}\sb\infty is employed to design feedback controller recursively using the identified model that is time varying in nature. The robust stability of the adaptive feedback system is then established

    System Identification and Robust Control:A Synergistic Approach

    Get PDF

    Non-Iterative Data-Driven Model Reference Control

    Get PDF
    In model reference control, the objective is to design a controller such that the closed-loop system resembles a reference model. In the standard model-based solution, a plant model replaces the unknown plant in the design phase. The norm of the error between the controlled plant model and the reference model is minimized. The order of the resulting controller depends on the order of the plant model. Furthermore, since the plant model is not exact, the achieved closed-loop performance is limited by the quality of the model. In recent years, several data-driven techniques have been proposed as an alternative to this model-based approach. In these approaches, the order of the controller can be fixed. Since no model is used, the problem of undermodeling is avoided. However, closed-loop stability cannot, in general, be guaranteed. Furthermore, these techniques are sensitive to measurement noise. This thesis treats non-iterative data-driven controller tuning. This controller tuning approach leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. A straightforward data-driven tuning scheme is proposed, and the correlation approach is used to deal with measurement noise. For linearly parameterized controllers, this leads to a convex optimization problem. The accuracy of the correlation approach is compared to that of several solutions proposed in the literature. It is shown that, if the order of the controller is fixed, both the correlation approach and a specific errors-in-variables approach can be used. The model reference controller-tuning problem is extended with a constraint that ensures closed-loop stability. This constraint is derived from stability conditions based on the small-gain theorem. For linearly parameterized controllers, the resulting optimization problem is convex. The proposed constraint for stability is conservative. As an alternative, a non-conservative a posteriori stability test is developed based on similar stability conditions. The proposed methods are applied to several numerical and experimental examples

    Direct data-driven H2 − H∞ loop-shaping

    Get PDF
    In this paper, a direct data-driven approach is proposed to tune fixed-order controllers for unknown stable LTI plants in a mixed-sensitivity loop-shaping problem. The method requires a single set of input-output samples and it is based on simple convex optimization techniques; moreover, it guarantees internal stability as the data-length tends to infinity. Compared to a standard model-based approach, the proposed methodology theoretically guarantees the same asymptotical performance in case of correct parameterization, whereas the direct data-driven formulation is less conservative in case of undermodeling. The effectiveness of the method is illustrated via some numerical examples

    Acoustic Echo Cancellation and their Application in ADF

    Get PDF
    In this paper, we present an overview of the principal, structure and the application of the echo cancellation and kind of application to improve the performance of the systems. Echo is a process in which a delayed and distorted version o the original sound or voice signal is reflected back to the source. For the acoustic echo canceller much and more study are required to make the good tracking speed fast and reduce the computational complexity. Due to the increasing the processing requirement, widespread implementation had to wait for advances in LSI, VLSI echo canceller appeared. DOI: 10.17762/ijritcc2321-8169.150513
    • …
    corecore