Robust system identification from weighted impulse response data and worst-case error bounds

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Robust system identification from weighted impulse response data and worst-case error bounds

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The paper describes the problem of identification of a linear discrete-time system in l1, from noisy impulse response data of the system. Many tuned algorithms using window functions are proposed in the literature for the problem of H∞ identification. The study concerns the use of suitable window functions and tuned and untuned algorithms for the problem of identification in l1. The properties of window functions suitable for l1 identification are analysed and it is shown that the use of a parameterised exponential window function leads to a convergent worst-case error. The optimal value of the window parameter which results in the least worst-case model error is given in terms of the a priori assumptions on the system and the noise. The tuned algorithm using the optimal parameter is proved to be robustly convergent.

Inspec keywords: discrete time systems; identification; transient response; robust control; errors; linear systems; noise; H∞ control

Other keywords: weighted impulse response data; robust system identification; untuned algorithms; convergent worst-case error; robust convergence; window functions; noisy impulse response data; tuned algorithms; worst-case error bounds; H infinity identification; least worst-case model error; H∞ identification; linear discrete-time system; parameterised exponential window function

Subjects: Stability in control theory; Simulation, modelling and identification; Discrete control systems; Optimal control

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