Disturbance Observer Based Tracking Control

An output-feedback observer is proposed in this paper to simultaneously estimate unknown states and disturbances of linear time invariant systems. The states are estimated using a Luenberger-like observer while the disturbance signals are estimated based on an inverse-dynamics motivated algorithm. The proposed schemes can be applied to a wide variety of disturbances since no disturbance model is required in the estimation. Depending on the input/output rank conditions of the plant, two different designs are proposed. The observer gains are selected based on sufficient conditions for exponentially converging estimation. The design procedure is illustrated step-by-step by using two examples: a hypothetical problem and the ground vehicle lateral speed estimation problem. A standard H∞-filter is used as the benchmark to illustrate the performance of the proposed method.

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This paper presents an adaptive algorithm to estimate states and unknown parameters simultaneously for nonlinear time invariant systems which depend affinely on the unknown parameters. The system output signals are filtered and re-parameterized into a regression form from which the least squares error scheme is applied to identify the unknown parameters. The states are then estimated by an observer based on the estimated parameters. The major difference between this algorithm and existing adaptive observer algorithms is that the proposed algorithm does not require any special canonical forms or rank conditions. However, an output measurement condition is imposed. The stability and performance limit of this scheme are analyzed. Two examples are then presented to show the effectiveness of the proposed schemes.

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