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This paper is concerned with the sampled-data based adaptive linear quadratic (LQ) control of hybrid systems with both unmeasurable Markov jump processes and stochastic noises. By the least matching error estimation algorithm, parameter estimates are presented. By a double-step (DS) sampling approach and the certainty equivalence principle, a sampled-data based adaptive LQ control is designed. The DS-approach is characterized by a comparatively large estimation step for parameter estimation and...
This paper is concerned with the sampled-data based adaptive
linear quadratic (LQ) control of hybrid systems with both
unmeasurable Markov jump processes and stochastic noises.
By the least matching error estimation algorithm, parameter estimates
are presented. By a double-step (DS) sampling approach and the certainty
equivalence principle, a sampled-data based adaptive LQ control is
designed. The DS-approach is characterized by a comparatively large
estimation step for parameter estimation and...
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