COMPETITIVE PARALLEL IDENTIFIERS IN ADAPTIVE CONTROL OF NONSTATIONARY STOCHASTIC SYSTEMS

Abstract The problem of identification of time-varying (linear) stochastic systems by parallel processing (through a bank of parallel adaptive filters) in application to adaptive control is addressed. The novel method, previously developed for adaptive signal estimation is in a straightforward manner adapted to self-tuning control. A simple discrete-time control-object model varying in correspondence with reasonable (in the Nyquist sense) changes in an analog prototype is considered in the simulation study. Results concerning applicability of the technique as well as its robustness to time-variability and tuning parameters are shown. An improvement regarding robustness to changes in the noise variance and to computational difficulties is also discussed.

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